{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fbc2d16-59f9-4be3-b93e-1a5440c7efd0",
   "metadata": {},
   "source": [
    "# Tutorial 6 - DIY KANs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afe6a1e-3ab4-4f3f-ad47-f6cd66419504",
   "metadata": {},
   "source": [
    "In all previous tutorials we've been using the `KAN` class from `jaxkan.KAN` in order to define a network. However, if one requires more fine-grained access over the network's constituents, they may define their own custom KAN class by using one (or more) KAN Layers from the `jaxkan.layers` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a2ef2a6-f681-427f-8252-ade2111ce0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jaxkan.layers.RBF import RBFLayer\n",
    "from jaxkan.layers.Sine import SineLayer\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from flax import nnx\n",
    "import optax\n",
    "\n",
    "from typing import List\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e70a5d-0340-4bdc-af4e-150d0098a87e",
   "metadata": {},
   "source": [
    "## Custom KAN Class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d239ed7c-6245-4f43-8a92-c4767f0dd654",
   "metadata": {},
   "source": [
    "In general, the custom KAN class should inherit from `nnx.Module` and contain an `__init__` and `__call__` method. Apart from these, there are no other pre-requisites. For example, if one does not intend to perform grid updates, there is no need to define a `update_grids` method.\n",
    "\n",
    "As an example, we will create a custom KAN class to perform the same function fitting task as in the first tutorial, this time using a mix of RBF Layers, Sine Layers and additional transformations in between."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f821497a-cd3d-458c-8a13-9dab2b85964b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomKAN(nnx.Module):\n",
    "    def __init__(self, rbf_layers, sine_layers, add_bias = True, seed = 42):\n",
    "\n",
    "        self.RLayers = [\n",
    "                RBFLayer(\n",
    "                    n_in = rbf_layers[i],\n",
    "                    n_out = rbf_layers[i + 1],\n",
    "                    D = 5,\n",
    "                    kernel = {\"type\":\"gaussian\"},\n",
    "                    add_bias = add_bias,\n",
    "                    seed = seed\n",
    "                )\n",
    "                for i in range(len(rbf_layers)-1)\n",
    "            ]\n",
    "\n",
    "        self.SLayers = [\n",
    "                SineLayer(\n",
    "                    n_in = sine_layers[i],\n",
    "                    n_out = sine_layers[i + 1],\n",
    "                    D = 8,\n",
    "                    add_bias = add_bias,\n",
    "                    seed = seed\n",
    "                )\n",
    "                for i in range(len(sine_layers)-1)\n",
    "            ]\n",
    "\n",
    "    def __call__(self, x):\n",
    "        for layer in self.RLayers:\n",
    "            x = layer(x)\n",
    "\n",
    "        # Apply a GELU transformation\n",
    "        x = nnx.gelu(x, approximate=True)\n",
    "\n",
    "        for layer in self.SLayers:\n",
    "            x = layer(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6157a1-d691-4ef1-90aa-115774b610b0",
   "metadata": {},
   "source": [
    "## Data Generation & Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166ad90d-430e-45ca-86a8-e6e4dbc1c943",
   "metadata": {},
   "source": [
    "Again, we create data for the function $f(x, y) = x^2 + 2\\exp(y)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b986e75a-6d4a-402f-bea7-36d13f4a7866",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x,y):\n",
    "    return x**2 + 2*jnp.exp(y)\n",
    "\n",
    "def generate_data(minval=-1, maxval=1, num_samples=1000, seed=42):\n",
    "    key = jax.random.PRNGKey(seed)\n",
    "    x_key, y_key = jax.random.split(key)\n",
    "\n",
    "    x1 = jax.random.uniform(x_key, shape=(num_samples,), minval=minval, maxval=maxval)\n",
    "    x2 = jax.random.uniform(y_key, shape=(num_samples,), minval=minval, maxval=maxval)\n",
    "\n",
    "    y = f(x1, x2).reshape(-1, 1)\n",
    "    X = jnp.stack([x1, x2], axis=1)\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db8bf4bc-49fe-4014-94aa-4e5dbf632369",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "\n",
    "X, y = generate_data(minval=-1, maxval=1, num_samples=1000, seed=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad91a42a-ffe8-4b8c-b1a7-cc9b60dfe5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f681d135-c885-4e59-87d7-1ea16a157c50",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529ae82e-b422-4ec7-b88a-f3be7cd99367",
   "metadata": {},
   "source": [
    "Let's now define an instance of the CustomKAN class, with 2 RBF Layers and 2 Sine Layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "471d7532-5434-498a-9b99-40896001776d",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_hidden = 5\n",
    "rbf_layers = [X_train.shape[1], n_hidden, n_hidden]\n",
    "sine_layers = [n_hidden, n_hidden, y_train.shape[1]]\n",
    "\n",
    "# Sanity check for dimensions matching\n",
    "assert rbf_layers[-1] == sine_layers[0]\n",
    "\n",
    "model = CustomKAN(rbf_layers, sine_layers, True, 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6723a412-c313-453c-aa88-9274687ee54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_type = optax.adam(learning_rate=0.001)\n",
    "\n",
    "optimizer = nnx.Optimizer(model, opt_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f18ab849-3c05-418a-a30b-3928f77c332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define train loop\n",
    "@nnx.jit\n",
    "def train_step(model, optimizer, X_train, y_train):\n",
    "\n",
    "    def loss_fn(model):\n",
    "        residual = model(X_train) - y_train\n",
    "        loss = jnp.mean((residual)**2)\n",
    "\n",
    "        return loss\n",
    "    \n",
    "    loss, grads = nnx.value_and_grad(loss_fn)(model)\n",
    "    optimizer.update(grads)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b06282a5-8899-4801-9c9e-d8b73b55d74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize train_losses\n",
    "num_epochs = 2000\n",
    "train_losses = jnp.zeros((num_epochs,))\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Calculate the loss\n",
    "    loss = train_step(model, optimizer, X_train, y_train)\n",
    "    \n",
    "    # Append the loss\n",
    "    train_losses = train_losses.at[epoch].set(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faab949e-dadb-4cec-bc13-63b10cb609c5",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86630d29-9b9f-452a-b2f1-399b02768829",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "\n",
    "plt.plot(np.array(train_losses), label='Train Loss', marker='o', color='#25599c', markersize=1)\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss Over Epochs')\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7798eb68-d6b5-47ec-b845-cf3d60dccf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE of the fit is 0.00062\n"
     ]
    }
   ],
   "source": [
    "y_pred = model(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print(f\"The MSE of the fit is {mse:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f061b2a3-b14c-4c82-a74d-c077016ca7a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hvv/++0GPNRoNffv2ZdasWcUGG+np6RdyORelT58+aDQaxo0bV+R/4YqiBOb1tG7dmri4OCZPnozL5QqcM23atH/Meh0XF0fHjh2ZMmUKx48fL/Iefv6cXCXJot2jRw82bNjA2rVrA8esViuff/45NWvWDJp7VBIWiwWPxxN0rFmzZqjV6n8cxoOCYUKn08n06dNZuHAh/fv3D3o+Ozu7SPu2bNkS4Lzlh4SE8MILL7Bz505GjRpVbE9JSXtPNBoN3bp14+effw5KPZCamhpIwBsREQEQGO5esWJF4Dz/vL4L1aNHD9atW8eGDRsCx9LT05kxY8YFl1kSERERxMbGBl0LwCeffFLk3NLcg0JcDBkiFFescw3RFdarVy86d+7MSy+9xNGjR2nRogWLFi3i559/5qmnngp8CbVs2ZKBAwfyySefkJubS/v27fnzzz+L5FmCgnQBS5cupU2bNjz00EM0btyYrKwsNm/ezB9//EFWVlaZX+v51KlTh9dee43Ro0dz9OhRevfuTXh4OEeOHGHOnDk8/PDDPPfcc+h0Ol577TUeeeQRbr75ZgYMGMCRI0eYOnXqP87BgoLg84YbbuDaa6/l4YcfplatWhw9epT58+ezdetWAFq1agXASy+9xD333INOp6NXr17FJkMdNWpUIN3GE088QXR0NNOnT+fIkSPMmjWr1FnflyxZwuOPP06/fv2oX78+Ho+Hr7/+OhAU/5Nrr72WunXr8tJLL+F0OoOGBwGmT5/OJ598wl133UWdOnXIy8vjiy++ICIigh49epy37FGjRrFnzx4mTpzIokWL6Nu3L9WqVSM7O5vNmzfz448/Eh8fX6Ikoq+99hqLFy/mhhtu4LHHHkOr1fLZZ5/hdDp5++23A+d169aN6tWrM3z4cJ5//nk0Gg1TpkwhLi6uSJBcUi+88AJff/01t956K08++WQgTUONGjXYvn37BZVZUg8++CBvvvkmDz74IK1bt2bFihXs37+/yHmluQeFuCgVsXRRiLJWOE3D+ZydpkFRCpb+P/3000qVKlUUnU6n1KtXT5k4cWLQknhFURS73a488cQTSkxMjBIaGqr06tVLOXHiRJFl4IqiKKmpqcqIESOU5ORkRafTKYmJiUqXLl2Uzz//PHBOadM0/FN6Af+y+/T09GKfnzVrlnLDDTcooaGhSmhoqNKwYUNlxIgRyr59+4LO++STT5RatWopBoNBad26tbJixQqlU6dO/5imQVEUZefOncpdd92lREZGKkajUWnQoIEyZsyYoHPGjx+vVK1aVVGr1UHL5c9O06AoinLo0CHl7rvvDpR3/fXXK/PmzStR+5xdx8OHDyvDhg1T6tSpoxiNRiU6Olrp3Lmz8scff5ynVYO99NJLCqDUrVu3yHObN29WBg4cqFSvXl0xGAxKfHy8cvvttyt//fVXicufM2eO0qNHDyUuLk7RarVKZGSkcsMNNygTJ05UcnJygs4FlBEjRhRbzubNm5Xu3bsrYWFhSkhIiNK5c2dlzZo1Rc7btGmT0qZNG0Wv1yvVq1dX3n333XOmaTj790ZRlCL3haIoyvbt25VOnTopRqNRqVq1qjJ+/Hjlv//9b5mlaSiuHoqiKDabTRk+fLhiNpuV8PBwpX///kpaWlqxv5/nugfP1abF3ZtC/BOVosjMPSGEEEKIsiRzsIQQQgghypgEWEIIIYQQZUwCLCGEEEKIMiYBlhBCCCFEGZMASwghhBCijEmAJYQQQghRxip1olGfz8fp06cJDw+X7Q+EEEIIcckpikJeXh5VqlQ5b8LjSh1gnT59OrCvlhBCCCFEeTlx4sR5N4mv1AFWeHg4UHCR/v21Khufz0d6ejpxcXGl3vpDXDhp94oh7V4xpN3Ln7R5xSiPdrdYLCQnJwdikHOp1AGWf1gwIiKiUgdYDoeDiIgI+SUsR9LuFUPavWJIu5c/afOKUZ7t/k9Tk+RTF0IIIYQoYxJgCSGEEEKUMQmwhBBCCCHKWKWeg1USiqLg8Xjwer0VXZVi+Xw+3G43DodDxunLUWVtd41Gg1arlbQkQghxmbuiAyyXy0VKSgo2m62iq3JOiqLg8/nIy8uTL81yVJnbPSQkhKSkJPR6fUVXRQghxDlcsQGWz+fjyJEjaDQaqlSpgl6vvyy/SP09bNIrUb4qY7srioLL5SI9PZ0jR45Qr169StX7JoQQV5MrNsByuVz4fD6Sk5MJCQmp6OqcU2X8or8SVNZ2N5lM6HQ6jh07hsvlwmg0VnSVhBBCFKPC//t76tQp7r33XmJiYjCZTDRr1oy//vqrzMqX/+GLK43c00IIcfmr0B6s7OxsOnToQOfOnfntt9+Ii4vjwIEDREVFVWS1hBBCCCEuSoUGWG+99RbJyclMnTo1cKxWrVoVWCMhhBBCVFY2h7uiqxBQoQHWL7/8Qvfu3enXrx/Lly+natWqPPbYYzz00EPFnu90OnE6nYHHFosFKJjQ7vP5gs71+XwoihL4uZz561cR9XzggQfIyclhzpw5AHTu3JkWLVrw/vvvl2s9li1bxs0330xWVhaRkZHl8p4V2e7/5JVXXuHnn39my5YtRZ7z39PF3feXO//vZWWrd2Un7V7+pM3LV2qWlXd/2MSOQxl8/lS7S9ruJS27QgOsw4cP8+mnn/LMM8/w73//m40bN/LEE0+g1+sZMmRIkfMnTJjAuHHjihxPT0/H4XAEHXO73fh8PjweDx6P55Jdw8VSFCWQo8s/2Xr48OF8/fXXAOh0OqpXr87gwYMZNWoUWm3ZfmT+L2l/G82cOROdTleiNlu+fDldu3YlLS3tooMifxuU1+dVuN1vv/12/vzzT1atWkXr1q1LXMZXX33Fs88+S3p6epnXz/+Pc3Ft4fF48Pl8ZGZmotPpyvy9LyWfz0dubi6KoshcsnIk7V7+pM3Lz7dLDjFt0QEcroJ/01duPUZnjeqStXteXl6JzqvQAMvn89G6dWveeOMNAK655hp27tzJ5MmTiw2wRo8ezTPPPBN47N/ROi4urshmzw6Hg7y8PLRa7QUHJR6nFY/LgTE8pshzjrxMtHojWkPoBZV9tsJflGq1mltvvZUpU6bgdDpZsGABjz/+OAaDgdGjRxd5rcvluuCcSGq1GrVaHWij+Pj4Er9Wo9EAXFQbX4qySiMlJYW1a9cyYsQIpk+fTtu2bUv8Wv8v76Wor1qtRqVSFVu2VqtFrVYTExNT6VYR+nw+VCrVJd3pXhQl7V7+pM3Lj085jMPl5dr68bw46DoSw5VAuzvystDqDWX2XQ2U+N/dCv3Uk5KSaNy4cdCxRo0acfz48WLPNxgMREREBP3A30HC2T8qleqCf7wuG9t/eZNtc17FmZ8V9JwzP4ttc15l+y9v4nXZLup9gKA//X83GAwkJSVRs2ZNHnvsMW655RZ+/fVXVCoVDzzwAHfddRdvvPEGVatWpWHDhqhUKk6ePMmAAQOIiooiJiaG3r17c+zYsUC5Pp+PZ599lqioKGJjY3nxxRcDw2P+czp37szTTz8deOxyuRg1ahTVq1fHaDRSr149pkyZwrFjx7j55psBiI6ORq1W88ADD6BSqVAUhTfffJPatWsTEhJCy5YtmTVrVtB1//bbbzRo0ICQkBBuvvlmjh07FlSPs38GDx7MPffcE3TM4/EQFxfH119/jUqlYtasWTRv3pyQkBBiY2Pp2rUrNlvxn4//vaZOncrtt9/OY489xvfff4/D4Qg6Lzc3l0cffZTExMTAKtf58+ezfPlyhg0bRm5ubuB+GzduHCpVwf+afv7556ByoqKimD59euDxqFGjaNCgAaGhodSpU4eXX34Zj8dTbB3P9XOu+/5y/6nMda/MP9Lu0uZXys+WA+nsPpqFWq3G57bTTvUbj7c4ytRn2tGibnyg3V3WbLbPfZUdv76Fz20v0zqURIUGWB06dGDfvn1Bx/bv30+NGjUqqEZ/87gcuO0W7LmpbJ09DkdeJlDQc7V19jjsuam47RY8Lsc/lFQ2TCYTLpcr8PjPP/9k3759LF68mHnz5uF2u+nevTvh4eGsXLmS1atXExYWxq233hp43TvvvMO0adOYMmUKq1atIisrKzD36lzuv/9+vvvuOz788EP27NnDZ599RlhYGMnJycyaNQuAffv2kZKSwgcffAAUDOV+9dVXTJ48mV27dvH0009z7733snz5cgBOnDhBnz596NWrF1u3buXBBx9k1KhR563H4MGD+fXXX8nPzw8c+/3337HZbNx1112kpKQwcOBAhg0bxp49e1i2bBl9+vQ57/wqRVGYNm0a9957Lw0bNqRu3br89NNPged9Ph+33XYbq1ev5ptvvmH37t28+eabaDQa2rdvz/vvv09ERAQpKSmkpKTw3HPPnfcaCgsPD2fatGns3r2bDz74gC+++IL33nuvxK8XQoirzcn0PJ78cAkDx81j7JTV+HwKHpcDjTuXZqb9bJvzKo68LAAceVkV8l0dRKlAGzZsULRarfL6668rBw4cUGbMmKGEhIQo33zzTYlen5ubqwBKbm5ukefsdruye/duxW63X3D97JYMZe20kcqSD/ora6eNVHJO7Q16bLdkXHDZfj6fT3G5XIrP5wscGzJkiHLnnXcGnl+8eLFiMBiU5557LvB8QkKC4nQ6A6/5+uuvlQYNGgSV43Q6FZPJpPz++++KoihKUlKS8vbbbweed7vdSrVq1QLvpSiK0qlTJ+XJJ59UFEVR9u3bpwDK4sWLi6370qVLFUDJzs4OHHM4HEpISIiyZs2aoHOHDx+uDBw4UFEURRk9erTSuHHjoOdffPHFImUV5na7ldjYWOWrr74KHBs4cKAyYMAARVEUZdOmTQqgHD16tNjXn83n8ykLFixQ4uLiFLfbrSiKorz33ntKp06dAuf8/vvvilqtVvbt21dsGVOnTlXMZnOR44AyZ86coGNms1mZOnXqOeszceJEpVWrVoHHY8eOVVq0aFHsuWVxb1cUr9erpKSkKF6vt6KrclWRdi9/0uZlJ8/mVP7z3Ualyf1TlXoDv1TqD/pS+ffnKxSr3aUoSvB39ZppTygHd21U1kx7oky/qws7X+xRWIXOwbruuuuYM2cOo0eP5tVXX6VWrVq8//77DB48uCKrFWAMj6Fln7GBKHjzTy8DYDIn0LLP2GLnZpWVefPmERYWFpisP2jQIF555ZXA882aNQuad7Vt2zYOHjxIeHh4UDkOh4NDhw6Rm5tLSkoKbdq0CTyn1Wpp3br1OXt5tm7dikajoVOnTiWu98GDB7HZbHTt2jXouMvl4pprrgFgz549QfUAaNeu3XnL1Wq19O/fnxkzZnDfffdhtVr5+eef+f777wFo0aIFXbp0oVmzZnTv3p1u3bpx9913nzen2rRp0+jfv39gntPAgQN5/vnnOXToEHXq1GHr1q1Uq1aN+vXrl/j6S2rmzJl8+OGHHDp0iPz8fDweT5F5hEIIcTXz+nzMWn6A93/YREauHYC2jZMYfV8bGtX4+/u38He1LTeVQ6u/RWtPJaQcvqvPp8K3yrn99tu5/fbbK7oa52QMj6FR1xGB4AqgUdcRl/wD69y5M59++il6vZ4qVaoUmewcGho8YS8/P59WrVoxY8aMImXFxcVdUB1MJlOpX+Mfwps/fz5Vq1YNes5gMFxQPfwGDx5Mp06dSEtLY/HixZhMJm699VagYJL84sWLWbNmDYsWLWLSpEm89NJLrF+/vtjcallZWfz888+43W4mT54cOO71epkyZQqvv/76BV0/EJiHVpjb/XdulrVr1zJ48GDGjRtH9+7dMZvNfP/997zzzjsX9H5CCHEl+uOv4/zfF6sAqJEQwYuDr6dLq+rFbm/m/67e9NPYwLHy+K4+H1na8A8ceZnsWfxx0LE9iz8OzMm6VEJDQ6lbty7Vq1cv0Sq1a6+9lgMHDhAfH0/dunWDfsxmM2azmaSkJNavXx94jcfjYdOmTecss1mzZvh8vsDcqbP5e9D86Q4AGjdujMFg4Pjx40XqkZycDBQsZNiwYUNQWevWrfvHa2zfvj3JycnMnDmTGTNm0K9fv6DVlyqVig4dOjBu3Di2bNmCXq8/5xyzGTNmUK1aNbZu3Rr045+n5vV6ad68OSdPnmT//v3nvP7C1+4XFxdHSkpK4PGBAwew2WyBx2vWrKFGjRq89NJLtG7dmnr16gUm+QshxNXM5f7739SurWvQvmkVRt/bhvkT+3BL6xrn3Du2or6rz0cCrPMoPKHdZE7g2rtfxWROKDLx/XIwePBgYmNjufPOO1m5ciVHjhxh2bJlPPHEE5w8eRKAJ598kjfffJO5c+eyd+9eHnvsMXJycs5ZZs2aNRkyZAjDhg1j7ty5gTJ/+OEHAGrUKLjZ582bR3p6Ovn5+YSHh/Pcc8/x9NNPM336dA4dOsTmzZuZNGkS06dPB+DRRx/lwIEDPP/88+zbt49vv/2WadOmleg6Bw0axOTJk1m8eHHQUPL69et54403+Ouvvzh+/DizZ88mPT2dRo0aFVvOlClTuOuuu2jatGnQz/Dhw8nIyGDhwoV06tSJjh070rdvXxYvXsyRI0f47bffWLhwYaB98vPz+fPPP8nIyAgEUTfffDMfffQRW7Zs4a+//uLRRx8NCgTr1avH8ePH+f777zl06BAffvjhPy42EEKIK1luvpM3vl7Hrc/9FMjGrlarmDr6Vh7o0RS9VnPO1579XV2nw6DL4rtaAqxzOPsDa9lnLOYqDWjZZ+xl8cGdLSQkhBUrVlC9enX69OlDo0aNGD58OA6HIzC359lnn+W+++5jyJAhtGvXjvDwcO66667zlvvpp59y991389hjj9GwYUMeeughrFYrAFWrVmXcuHGMGjWKhIQEHn/8cQDGjx/PmDFjmDBhAo0aNeLWW29l/vz5gaG66tWrM2vWLObOnUuLFi2YPHlyIBfaPxk8eDC7d++matWqdOjQIXA8IiKCFStW0KNHD+rXr8///d//8c4773DbbbcVKWPTpk1s27aNPn36FHnObDbTpUsX/vvf/wIwa9YsrrvuOgYOHEjjxo154YUXAr1W7du359FHH2XAgAHExcXx9ttvAwWrNZOTk7nxxhsZNGgQzz33HCEhIYH3uOOOO3j66ad5/PHHadmyJWvWrGHMmDElun4hhLiSeLw+vlm0m67P/Mi033ZxMj2f3zccDTx/rh4rv7O/q1v0HkNodDVa9B5T4d/VKuVcM5wrAYvFgtlsJjc3t9hEo0eOHKFWrVoXlIzR47Sy7ecJuO2WIpPk/B+ozhRBiztHX1QCM+V/2bq1Wu0/3kii7FTmdr/Ye7si+Xw+0tLSiI+PL3EuGXHxpN3Ln7T5P1ux7SQTvlnPoVM5ANStGsmoe9vQsUW1Epdx9ne1PjQq0O4ua3aZfVcXdr7Yo7AKn+R+udIaQmlx5+hiM7n7VyyUZSZ3IYQQ4kpxvp1Q8rLTeWryRlbuKJirGhlm4Ml+1zLg5oZoNaULRs/+ri68T2BFf1dLgHUeWkPoOT+UilyZIIQQQlxu/EGVVm8sMgLk317O43Kw65fxuDLroNVEcP+tjXmsd0siQi98lfnl+l0tAZYQQgghLkrhobpG3UYG7YRS9+YRfDRlJtdVsRKlycOZn8k9dXWM+dcT1K9T8Tu3XCoyMCyEEEKIi1J4e7k9iybRqNtIjBEJrD7o5o6X5vHtvqp8tRHsOWcwmRPoMvjfV3RwBRJgCSGEEOIi+ec7+VfuLfzpCz440J7PDrUmwxNJmNpK01g7oTHVKjS7enmSIUIhhBBCXDRjeAzVurzIuA9msOJUNApWtCovN4b9xR11swkxqGnU9dWrIrgC6cESQgghRBmZtfYMy0/FoKCiVdQpnkmYyt11ThNiKAg3Kjq7enmSAEsIIYQQF0RRFLLzHIHH995UlZZxuTzbYDX9QmcSqWRgt5yhSY9nKjzxZ3mTAEsIIYQQpbbtYDoDx83j4YmLUBQFR14mBxa8wcM1VpLk3k54bC30oWZCopM5vHoGjbqNvKqCLAmwrjKvvPIKCQkJqFQq5s6dW9HVAWDo0KH07t27oqshhBBXPY/Tes7Ax5GXicdp5Uymlec+WUa/l39h8/409p/IZue+o4Eta0IiE0lo2InwhNp0GvEdYTHJQasLTeYEdKYItPrKtRNFackk98vQ0KFDAxsj63Q6qlevzv3338+///1vtNoL/8j27NnDuHHjmDNnDm3btiUqKuqi6/rKK68wd+5ctm7d+o/njRs3rsjxxYsX88EHH1B4x6abbrqJli1b8v777190/YQQQpTMP20Rt/6H8fx2shoLjsTicBXsyXpXx3o83b8VsaGwbXvBtjH+7On+7Oot+4wNbFkTGl3lqtkJRQKsy9Stt97K1KlTcTqdLFiwgBEjRqDT6Rg9enSpy/J6vahUKg4dOgTAnXfeWSH77zVp0oQ//vgj6Fh0dDR6vb7c6yKEECJY4VxWW2ePC8rC/vs3E3h9XT1y3EbAS6sGCfz7vjY0qx0XeP3Z28v5A6izt6y50gMrv6tyiNDmcJ/zx+nylPhcRwnOvVAGg4HExERq1KjBv/71L2655RZ++eUXAJxOJ8899xxVq1YlNDSUNm3asGzZssBrp02bRmRkJL/88guNGzfGYDAwbNgwevXqBYBarQ4KsL788ksaNWqE0WikYcOGfPLJJ0F1OXnyJAMHDiQ6OprQ0FBat27N+vXrmTZtGuPGjWPbtm2oVCpUKhXTpk075zVptVoSExODfvR6fdAQ4dChQ1m+fDkffPBBoMyjR49ecDsKIYQombNzWW2dPY7c0/vYOnscYa4TmI0+qsaG8OGTN/Ptyz2DgisoCKjOlYLBGB5z1QRWfldlD1bLYV+d87lOLavxxQvdA4/b/etb7E5Psede3yiRb8b0DDzu/OQPQaspAPZ/O/wia1vAZDKRmVkwLv7444+ze/duvv/+e6pUqcKcOXO49dZb2bFjB/Xq1QPAZrPx1ltv8eWXXxITE0NSUhI33XQTDzzwACkpKYFyZ8yYwcsvv8xHH33ENddcw5YtW3jooYcIDQ1lyJAh5Ofn06lTJ6pWrcovv/xCYmIimzdvxufzMWDAAHbu3MnChQsDPVNms/mirvODDz5g//79NG3alFdffRWAuLi4f3iVEEKIsuAPshZ/M4EZW8Lol/UKeo2P0MgEJo+6k2pVq2DQX5WhQ6lJK13mFEXhzz//5Pfff2fkyJEcP36cqVOncvz4capUqQLAc889x8KFC5k6dSpvvPEGAG63m08++YQWLVoEyoqMjAQgMTExcGzs2LG888479OnTB4BatWqxe/duPvvsM4YMGcK3335Leno6GzduJDo6GoC6desGXh8WFhbomfonO3bsICwsLPC4cePGbNiwIegcs9mMXq8nJCSkRGUKIYQoO/k2F5PnH2ba2sa4PApmvZPbqx2mUdcRmKtUr+jqVSpXZYC1dcr953xOow6em7T200HnPFd91rlLP+h/cRUrZN68eYSFheF2u/H5fAwaNIhXXnmFZcuW4fV6qV+/ftD5TqeTmJi/u2b1ej3Nmzc/73tYrVYOHTrE8OHDeeihhwLHPR5PoCdq69atXHPNNYHg6mI0aNAgMMwJBcOgQgghyofHaQ2aI1WYNTeDX9af5sPZO8i0FIzENIzIpGVUGlCQIPRq2eKmrFyVAVaIUVfh5/6Tzp078+mnn6LX66lSpUpg9WB+fj4ajYZNmzah0WiCXlO4d8hkMv3jRPb8/HwAvvjiC9q0aRP0nL9sk8l00dfip9frg3q/hBBClI/zrRBcvnEvr37xByfyC/69Twxx0Cd5D21qqGnc7QX2LP64yMR38c+uygCrMggNDS02GLnmmmvwer2kpaVx4403XtR7JCQkUKVKFQ4fPszgwYOLPad58+Z8+eWXZGVlFduLpdfr8Xq9F1WP8ihTCCGuBufqpfK4HDgsaTjzs9g6exzNbn8ejT4EgM+/nc+J/EhCtB7urHWK9uG7CI9O4Jq+44LSLEiQVTpX5SrCyqx+/foMHjyY+++/n9mzZ3PkyBE2bNjAhAkTmD9/fqnLGzduHBMmTODDDz9k//797Nixg6lTp/Luu+8CMHDgQBITE+nduzerV6/m8OHDzJo1i7Vr1wJQs2ZNjhw5wtatW8nIyMDpdF70NdasWZP169dz9OhRMjIy8Pl8F12mEEJc6fy9VOfLkm7LPk1a6hl+eXcIG755mk0zR9M7aQfdqqczb/wt9KhnxZlzDI3eFEgEWnh14dWQILSsSIBVCU2dOpX777+fZ599lgYNGtC7d282btxI9eqln4D44IMP8uWXXzJ16lSaNWtGp06dmDZtGrVq1QIKepMWLVpEfHw8PXr0oFmzZrz55puBIcS+ffty66230rlzZ+Li4vjuu+8u+vqee+45NBoNjRs3Ji4ujuPHj190mUIIcaU7O4+VP8hy5GUW9EDlW9ik6sLYnTczN/Va0vavJXX/GqpE6/nPSw8RGx2J12UjJDoZxevG4/p7Vbw/yGpx5+irLt3ChVIphVNoVzIWiwWz2Uxubi4RERFBzzkcDo4cOUKtWrUwGi/faFtRFDweD1qttkKSf16tKnO7V5Z7uzg+n4+0tDTi4+NRq+X/d+VF2r38VUSbe5xWrFmn2bNoEvbcVEzmBBp1HcGexR+z/rCT2aeakWIrmGdVxWTh0ajpaL1W4uu3o8WdLwXmWpnMCZV2GLA82v18sUdhMgdLCCGEqOQKT2Jv1G1kIMia99Vb/Hi0Hrst8QBEhmq5s/pR2kTsQeWtTe6Z/WQc2cSGb55FozcSElWl0gZXlxv5r4wQQghRifl7rvzDg3sWTaJ2h8FszYrj1e3t2W2JR6Py0cm8jaejP6Ft6DbCIuNpfc8EYmu1wutykHFsC7mn91HvpmESXJURCbCEEEKISsrfc7Vn0SQadRuJyZxAfuYJ1k0bQS3tEUJV+TTS7uLphGn0MC9H58rCmnWcRt1GYgiPLSjD7UDxuHFYs9i14L1zTpAXpSNDhEIIIUQl5Z/YbstJ5euvp7PX14mueRNw5WfjyT7NyOijhJBHZHxTnFY3KrWakOhkds6fiNftJOPIJvSmcNRaPRqdEbczX1IxlJErPsCqxHP4hSiW3NNCCD9jeAyG1k8y4ZN57MkOA3KoUrUptTyL0OpMmPVw/eAvObl1HtZsEzpDQULq1P2r8bldqHV6Eht2pGnP59HojOycP1HyXZWRK3aIUKcryKpus9kquCZClC3/Pe2/x4UQVw6P0xoYoiv8dyhIt+BxWgN/T0nN4N+fr+SeN5axJzsMndrHzRHrqWpdhlZnQqM3EpFYn5Nb59Go20hCo6oSGpNMgy6PolbrUOv0RCTWp2mPZzEn1SMsNlnyXZWhK7YHS6PREBkZSVpawT5KISEhl+Vy/MqcLqAyq4ztrigKNpuNtLQ0IiMji2yVJISo3AqvBGx2+/Ps/fOzwNY2AFtnj0NniqB2x4eY+OnX/Ho4Aae3oJ/k9va1GdpWy8GZ7+F1a9DoTVw/+D+c3LogMPG9UbeRgV6q8MQ6KD4fGq0+aJ9Bf74rrd4o+a4u0hUbYAEkJiYCBIKsy5GiKPh8PtRqdaX5or8SVOZ2j4yMDNzbQogrR1Ci0DnjAXDZctk0c3Tg7163g52/vs76U41wetU0qxXF/w3pQO1wC8s/HojP40ajN2FOrM/JrQuCUjbsnD8xUE5oVNVAjqyzhwRlWLBsXLGJRgvzer243e5yrFnJ+Xw+MjMziYmJkQSA5aiytrtOp6vUPVeS8LJiSLuXvwttc0deJpt/HIM95wzGiFi8bgcZRzZxwpVI47rVMWh8uJ35nKQ+Yc0GcFfnFrisWWz+cQxp+9eg1mppO/RjDq+e8Xey0W4j2Tl/ItkndmKKSiI0qmogmApkea/kCUb9JNFoOdNoNJftl5LP50On02E0GuUfvnIk7S6EuCwpPkCFNatgi7Asl5Efs25jq60RPdjBjbpFGEIi6TvyfcJikwMBkjM/k/j67Wly29OYk+oRkVA3EDgVDA8+zqFVM/C67UFBVOHNnGXeVdm6KgIsIYQQ4nLncVrZ9subZBxej2JM4Md9May0XofbV9BBkJFrxWXMJqbGNWgNIQBo9UZ0poJelPMFThEJdbim78t4XI4iPVQy7+rSkABLCCGEuEQ8Tis+j6vYYTdHXmZQUONxOfB63Gx1X8fPRxqQp4QDUFN3gq7a2VTRpIBah1rz91e31hBKiztHlzhwOlcAVZmHBS9XEmAJIYQQZczjtOKwpLN9xUd4/rcS0B/EFKRbsLFz/kR0pgha3DkarSEUY3gMS9w9+ebESQCi1FncGraUup71qFQKGl0ICQ1vwOt2BE1K1xpCJXC6DEmAJYQQQpQhj9PK9l/fwuIAvSsdty0nEBABbJo5+u8J54DbaUdrCMWRl0kzz2LCtLXpGP4X1+vWgD0LRatFrVYTWa0J+v8NB0oy0MufzO4VQgghylBBuoU8PM6CpMD6EDP23FQ2zRzNhm+e4cy+VTitWSjGeJYqd/PGD3uBgvlUieEK7964i6eH9Ubtzge1Gr0xjOjk5rismWiN4bToPUaSgVYC0oMlhBBClCFjeAwteo9h47xPcGWexhASgUZnIHX/arwuJ263k+3qTizd3I7s/AMADLmtKdUivHhdNjSKi62zXkGjN6LT6AiPr43DkoopIhGvy4bWECKT0isB6cESQgghypgxPJpa7e/BZE7AZcvFmZeFz+3isLcOk/NGMDuzM9n5bmomhPHZ812pGu5h6+xxWDNPcHrHYlzWXAyhkdz4yDQiEuoQEp2M3XIGe07B0CCce8K6uDxUaID1yiuvoFKpgn4aNmxYkVUSQgghyoTeFEHDW/6Fz+PmVGo6X2X35b+Z/TnjicOkdtAveRcvN1/NtQl2ts15FWvmSWzZKehNZgzhUXQa8R3x9drQss9YwmKSCYlOxpp1HJVGJ0ODlUCFDxE2adKEP/74I/BYq63wKgkhhBAXzWW3cPDP98g9sx+tR+GEpyoalY/OVdPoHrsNleUwzuzqbP7pZQBMkYmExdfC67LR5LZnMCfVA4JzWoXH16bFHaOk96oSqPBoRqvVyr5qQgghrih5ORl8N3MWdTPXolIpVG/YikcsxzGTQfUYHRCJlQRyz+zHnFgftVZHk1ufJDSmmiQDvUJUeIB14MABqlSpgtFopF27dkyYMIHq1asXe67T6cTpdAYeWywWoGDbE5/PVy71LWs+ny+w8bAoP9LuFUPavWJIu5cfRVFYvHYPE6YvJ8Uaz72JLbi5cQjX9nud1sC2ueOx56ai0Rmx5ZxBrdaDWoOCit2LP6FF7zEYw6OL/az0oVEA8jmeR3nc6yUtu0I3e/7tt9/Iz8+nQYMGpKSkMG7cOE6dOsXOnTsJDw8vcv4rr7zCuHHjihzfv39/sedXBj6fj9zcXMxms+yJV46k3SuGtHvFkHYvH4dT8vj4l91s3JcBQJjex8he9ehybZVA/iqX3cLBlV9hzTiG1mTGGBZD9Va9OLllAU5bDoaQSGq1vydwviid8rjX8/LyqF+//j9u9lyhAdbZcnJyqFGjBu+++y7Dhw8v8nxxPVjJyclkZ2ef9yIvZz6fj/T0dOLi4uQfvnIk7V4xpN0rhrT7hfE4rXhcTozh0UWec+RlodUb0BpCybI4+HDWZn5Ysh+foqDTqhncpS69r4umfv0GQW3uyMti848v4czPIiSqSqDHypGXFejdMpkTAsdF6ZTHvW6xWIiKivrHAKvChwgLi4yMpH79+hw8eLDY5w0GAwaDochxtVpdqf/RUKlUlf4aKiNp94oh7V4xpN1Lx+O0suPXt3Cftc0NFGx1s33uq4Ftbp78cBkb954BoPv1NXl+4HVUiwsjLS2tSJvrjSZMEXFodQZa9nk5UG6IOZZr+rz8v82Zw9EbTfJZXaBLfa+XtNzLKsDKz8/n0KFD3HfffRVdFSGEEFexgmzsliJb0jjyMtkyaxzW3DTC/nfe432v4e0ZGxh9Xxuub5QEnHueTmk3ZxaVV4WGx8899xzLly/n6NGjrFmzhrvuuguNRsPAgQMrslpCCCGucv5gx2ROCARZuaf3MXfa27y2NpklWU0DQVe7JlWY9dqdgeDqn/g3dj7X+0pwdWWo0B6skydPMnDgQDIzM4mLi+OGG25g3bp1xMXFVWS1hBBCXIUK5lz93bNUOP/UqZR0Pn3ze9Zl1ENBRQZ6xhrMgdeq1aqKqra4TFVogPX9999X5NsLIYQQQEFwte3nCUXnXBnMrHB35asdJ3EpegC6XxPH6AduxqC/rGbZiMuM3B1CCCGuesXNudpx0sUzk/4kNccJ6KmuP82A2kdoFh9GtKEDEFbR1RaXMVmiIIQQ4qpX3JwrTe4BMnJsmNW5DI5fzIyXutKsRljgeUdeZkVXW1zGJMASQghxRfI4recMghx5mXic1qBjWU4DB+OGYDInkJ95gqOz/8WQyB8YVX0mT704noR6bYsEYRJkiXORAEsIIcQVxz+nqrggyJGXydbZ49j28wQ8TitWh5v3f9hE92d/YvyMragaDUGlVqNW66gflsKND3xYZONlkzkBnSkCrd5YEZcnKgGZgyWEEKLSO3sFYOE5VZtmjqZF7zGExSYHgit7bio+BWYt28ekn/eRlmMDoFW9GE5s/J4YtZbwxDooPh+H18wgIrFukdWFkq9KnI/0YAkhhKjU/L1Vm38cQ27KgcDQX9Oez6PRGUndv5olH/Qldf9aNs0cjTX7FEec1Xhz9w2Mmb6FtBwbyfHhvPvI9TxRawkx3uOYzAm07v8GYTHJxQ4HSr4q8U+kB0sIIUSl5nE5cOZnkbZ/DekH1xFXrx0oCqAi68R2fF4P1qyTrP58OIoK0Jv5+OQt5LnzCDPpGHHXNfRrn8DuX1/DbinYC9CfqsGfB+vsjO5C/BMJsIQQQlRa/t6qJrc9TcbhDTjzsjmx+VciEuqSe2Y/KrUWxevBrQpFseWg1RnQaXT0qrKXM74qjHniPqpWrYLHaUVnKti4t3AQVTjIkjlXojQkwBJCCFEpnZ0ctNOI71j+8UCcednknN6D4vPi8djZ7G3HMldXeoUtpLFqGxqPi64N7bQa8GAgkJI9AkVZkzlYQgghKqWzk4MawqJpO/Rj1Do9Pp+PA85afGZ/hgXOu7ApYWxxXoveZEalLv6rT/YIFGVJerCEEEJUSmfPkdo0czRet5NUdyTzrT054G0EgEllo0voKrrWsaLXNUJnCsNly5U5VeKSkgBLCCHEZens1AuFOfIy0eqNgSBr08zRpO5fzfKc5vye/wA+NKjxcp12NZ0MfxJh0mHPDEGXWB+NzohGZ5Q5VeKSkgBLCCHEZeecmy/zd6JQnSmCFneOBsDrduJ1OYjjJD40NDIdpnfSDvTpa0BR4XY4CY+rhS3rBACmiDgadnlEhv3EJSNzsIQQQlwWCm9tc/b8Kn9+q8KJQl02C3/+dZSflh0g5/QePG479Y0neST8E+6L/Iko3wlial6L1mDCFB6Hy5aLyZyILesEdks6O+ZNlK1uxCUjPVhCCCEqXHE9Vv75VfmZJ1j+8UBia18PKDjzs8hQ1+DXEzewbtE6jBovLzetRagCEYn1MKYewO3QoFbrCI+rybX9Xufgiilo9CF4XXYikhrgddlkiFBcUhJgCSGEqHBn91j5g6xG3Uay/OOBuKy5nNm7HFVMUxZmXsvyU7H4lHS0Kh+dEo4TE5vEdcP/g9YQgsdpY9vc8TjyC3qnIhJqc22/19DqjXhcjqA/ZYhQXCoSYAkhhKhw/h6rzT+OwZp5kq2zx9Go6wj2LP4YU0QiDqeLDd4uLNzZAIdXB0C31sn0iFpDtPqseVrhMbQaMCEoOag/kDr7TyEuFQmwhBBCXBa0eiMavQlr1nEANv/0Mj6PG7vlDO6IRvyypxE+NNQMtzL2ke50uLYBHmcHSQ4qLksSYAkhhLgseFwOFK8bY0QCuWf24zY3Qpe7n5CoqoRbD9EzqSohnlQ6Vrej2XEQR72x500AKvmtREWSVYRCCCHKVeHVgoUZw2Oo3eE+zmRYmJnRhbG7unI8P5zc03swRiTQr7mH50a9QnhstcBcLVkFKC5XEmAJIYS45PxBlX+1YOHgyH889dhexr/3JW+dGswWR3N8aNjvrI5ao8NhSaVRt5GYk+rRss9YTOYEWQUoLmsyRCiEEOKSKpyCoVG3kUGrBRt1G8mu3yex5nQEM/fEkuu9HoDGVfXcbl5KdX0++RlGTBGJ7Fk0KSiFg8yvEpczCbCEEEJcUoVTMOxZNIlG3UayZ9GkQH6raRm92WlJAiBSm8dTdzWhVu6vOCxWIIyIxPrYsk4QUaVBoMdK5leJy50MEQohhLik/D1OJnNCIMiq3WEwtqwTuKy51FHvxqD20LfOSWaNbkdty684LKmYzAlce/erhMUkExKdjNdlw+NyVPTlCFEiFx1gWSwW5s6dy549e8qiPkIIISqRc01Yh7/nVsHfQZYqNInvdoYz45uvMUUkotbpuaWOg/EtV/H0/bdyasMM7LkFwVXLPmMxV2lAyz5jCYtJxpmfJRPbRaVR6gCrf//+fPTRRwDY7XZat25N//79ad68ObNmzSrzCgohhLg8FTdh3c+/Z+C2nyfgcVrx+nzM25TJ6A3X8tup2vxwtAHZ6ScIi62JXqfDrHdxcOU0VBpdILjyDwMW7gGTie2isih1gLVixQpuvPFGAObMmYOiKOTk5PDhhx/y2muvlXkFhRBCXJ7O3t6m8KpA/4bMbruFtTtO0Oeln/n35yvJzHMTZ7DSI/wPVG4bDksqTW57BpM5AWd+Fl6XjUbdRp4zcWiLO0fLxHZRKZQ6wMrNzSU6OhqAhQsX0rdvX0JCQujZsycHDhwo8woKIYSoeMUNBfqDHn2IGVv2abbOHkfu6X2B4CpPl8z01FsZ/u5q9hzLIkTrpW+13Txb5RvaJHswhJkJja7O4TUzaNRtZCDI2rNo0jnzZElwJSqLUgdYycnJrF27FqvVysKFC+nWrRsA2dnZGI3SbSuEEFea8w0F+tmyT2PLPs3mn14OzKEKu3Y4f25NQa2Cm5MzeLXFCno1tFO10Y1EJNSh04jvCI2pFrS6UIYBxZWi1GkannrqKQYPHkxYWBjVq1fnpptuAgqGDps1a1bW9RNCCFHBzh4K9M+P8g8Fumy5RCTWxWGzcsYeTnJoHo26jsBcpQEj+9q55ZpEbH99gtseHchf5d8/sGWfsYFNmUOjq0h+K3HFUCmKopT2RX/99RcnTpyga9euhIWFATB//nwiIyPp0KFDmVfyXCwWC2azmdzcXCIiIsrtfcuSz+cjLS2N+Ph41GrJmlFepN0rhrR7xSiLdi88r8pkTqBR1xHsWfwx9txU9CFmdmSa+X5/NbJcRsa3WEVCXFTQRHWP01rspsz+sq+0oEru9YpRHu1e0tjjgt69devW9OzZk1OnTuHxeADo2bNnuQZXQgghys/Zuaz8Q4EZSgLvbavPu9sactoehsFgJFOdXGTiu9YQes7koDK3SlyJSh1g2Ww2hg8fTkhICE2aNOH48eMAjBw5kjfffLPMKyiEEOLyYAyPoVHXEQDku3V8e7geY9Y3Z3tmFBqVwn1d6vDH+/cw6KFnA4GY5K0SV6tSB1ijR49m27ZtLFu2LGhS+y233MLMmTPLtHJCCCEunZImCS18bM/ij3F6NbyyvT3L02rhQ0XL2GzmjOvKmOE3YQ4zSN4qIbiASe5z585l5syZtG3bFpVKFTjepEkTDh06VKaVE0IIcWkU3oC58Fwp+Hu+lc4UEcg7VXgOVmR0Aj3b1mL1lkP0qbqdllWhZkJYUPmyIbO42pW6Bys9PZ34+Pgix61Wa1DAJYQQ4vJV0iShHpeD7XsOM+jfX7P/tC2QZX30sK78MvEeWtU04rLlFjsUKHOrxNWs1AFW69atmT9/fuCxP6j68ssvadeuXdnVTAghxCVz9qT1s5OEmswJJN/yIq99v4f+ry1jZ2Y4v6Q0CfR2mQxaQs2xMhQoxDmUeojwjTfe4LbbbmP37t14PB4++OADdu/ezZo1a1i+fPmlqKMQQohLoHAeKv/KQABNWCKbQ+7hXy//idXuBqD7dck82bvhObewkaFAIYKVugfrhhtuYOvWrXg8Hpo1a8aiRYuIj49n7dq1tGrV6lLUUQghxCVSeGUgwLbsOF7edB3vz9mN1e6mae1Yvn25J5Oe7kbdWtXPWYYEV0IEK3UPFkCdOnX44osvyrouQgghypl/ZaBfltPI6Wwn8ZFGnr3neu68oS5qtcyvFaK0Sh1g+fNenUv16sX/D0cIIcTlxZGXyZJvX+dMpp3GVQuys+t+/xivspdudd20ueZWCa6EuEClDrBq1qx53tWCXq/3oiokhBCi7HndDhx5WYSYYwHIzkzlrfe/ZN6RhkQa3Pw67HbMiTVoffdYtP+bk1V430EhROmUeg7Wli1b2Lx5c+Bn/fr1TJ48mfr16/Pjjz9ecEXefPNNVCoVTz311AWXIYQQoiiP08qR9T+ybe54bLkZ/LzqIHe+/CezDyXi8mmI0LvZ+PsUPE6rJAkVooyUugerRYsWRY61bt2aKlWqMHHiRPr06VPqSmzcuJHPPvuM5s2bl/q1Qgghzr+ZsjU7Bbc9nz0nHYwb9S2HcwsmpCdFG+mdtIMWYUcIIQGPyxHYM1BWBgpxccpsq+kGDRqwcePGUr8uPz+fwYMH88UXXxAVFVVW1RFCiCvW2Vvc+LOyb509jtyUA0Fb3DjyMtm7+GNOWUOYuOt6DueGYtR4eey2Goy/dgMtw48QEplQZChQVgYKcXFK3YNlsViCHiuKQkpKCq+88gr16tUrdQVGjBhBz549ueWWW3jttdfOe67T6cTpdBapi8/nw+fzlfq9Lwc+nw9FUSpt/SsrafeKIe1+8TxOK9t/fQu3PY8WvcdgDI/G5bDjsueRn3mSZR8PIqFhR67t+wouh4Mdv7yGLTeNajExdGgUhdZykB4JOzFnL8EDmMyJNO89Bn1olHwuZUju9YpRHu1e0rJLHWBFRkYWmeSuKArJycl8//33pSrr+++/Z/PmzSXu+ZowYQLjxo0rcjw9PR2Hw1Gq975c+Hw+cnNzURQFtbrMOhTFP5B2rxjS7hfPZbdgdetxujRsnPcJtdrfg94UQfx1Q0ibPxGPsQopp08z+Ydl/LTqFE+21BMT04iYpj0Z1zEBd151Dq0+g+d/5cVfNwiL3YPFnlah13WlkXu9YpRHu+fl5ZXovFIHWEuXLg16rFariYuLo27dumi1JS/uxIkTPPnkkyxevBijsWSTKEePHs0zzzwTeGyxWEhOTiYuLo6IiIgSv/flxOfzoVKpiIuLk1/CciTtXjGk3ctCPLHRj7Ft7njsmbs5teJjGt7yL45snE6oxsW2bJifcx0nt6YBOtakRDFu0EPkOTyEG7XsWPI1WntqoLS0jV8HesJE2ZF7vWKUR7uXNGZRKYqiXJIa/IO5c+dy1113odFoAse8Xi8qlQq1Wo3T6Qx6rjgWiwWz2Uxubm6lDrDS0tKIj4+XX8JyJO1eMaTdy07hTZkB0hwm5p5uzqY0MwAmjZue1Q7zzL8eILpaA04eO8ipFR/jyD2DyVyQ82rP4o8D+w5KOoayJfd6xSiPdi9p7FGiLqdffvmlxG98xx13lOi8Ll26sGPHjqBjDzzwAA0bNuTFF1/8x+BKCCGuZv4tbjb/9DKzj9fjj5QaeBU1apVCx/gT9Kp2iDCdm4NLP6VB18c5suZ7vLmphBQKpgrvQyg5r4QoWyUKsHr37l2iwlQqVYkTjYaHh9O0adOgY6GhocTExBQ5LoQQIljhLW5cPjVeRU0D03H61z5MvWpRNOo6JtBDtXvh+2Cq+r+eqpcDQVThIEtyXglRtkoUYMkqCCGEKB/ny2flyMtEqzeybPMxcjZPJcZbMLz33NDu1Jn2H2orO9HbzTTq9h3mpHqB4MmWm4ZKF0vDriOKlCs5r4S4NC5os+dLZdmyZRVdBSGEqDD+fFZuu6XIcJ0jL5P5X73Jd/ursj09nIYRcYxu6wwER52urU3q3hOERldnz6JJQcOAW2a/itcUTmhUUrHvK8OCQpS9CwqwrFYry5cv5/jx47hcrqDnnnjiiTKpmBBCXMmK66nyuBy47RasmSfZ/OMYru03HmN4DCkppxn//tcsOVEfH2o0KoWaUV6a9h4TeH2rfq9izTrNnkWTgob7jOExtOg9hmxLvvRQCVGOSh1gbdmyhR49emCz2bBarURHR5ORkUFISAjx8fESYAkhxD84V0+VMTyGRt1GsvzjgeSlHWbDzJfZFdaXzxYcwOYp2KS5c4tEnr+nFdWidUHBmdYQGhgWPHu4zxgejcbuQQhRfkq9hvHpp5+mV69eZGdnYzKZWLduHceOHaNVq1b85z//uRR1FEKIK4J/ixt/T5V/9Z4jLxNHXib5GSfYs2gSIdHJqLValh3S8t4vR7B5tFQPt/PF0x347MWe1K2ReM5hPdniRojLQ6l7sLZu3cpnn32GWq1Go9HgdDqpXbs2b7/9NkOGDLmgzZ6FEOJKd3avVeEUCZtmjsbrdmI5cwCNuSpRMck0v2MUIfPeY0NWFm3iUhjx6MNEV2tY0ZchhCihUvdg6XS6QPKu+Ph4jh8/DoDZbObEiRNlWzshhLhCnN1rBdCyz1j0IWZS96/m0J5tfHf6eibs7kTdm0dwePUMNGqFZ5v8xQ3xp9j/5ydBGzwLIS5vpQ6wrrnmmsDegZ06deLll19mxowZPPXUU5K/SgghzsG/os9kTggEWc68DBxOF0tyruWdzEfYaG3GGZuJH7//OpBh/dq7Xw16jQRZQlQOJQ6w/AlE33jjDZKSCpb6vv7660RFRfGvf/2L9PR0Pv/880tTSyGEuAIUDrJsOal89smH/Htdcxbl34RL0VMjJIcRVeZQX7M7sH2NuUqDIoGZBFlCXP5KPAeratWqDB06lGHDhtG6dWugYIhw4cKFl6xyQghxpTGGx5B84yM8+OYCDuUXbLAcpXfyVN9mJKd9S8aBk1iztLQe+JZkXBeiEitxD9aIESP46aefaNSoETfeeCPTpk3DZrNdyroJIUSl4bCkk5tyoNjnclMO4LCkF5yXl8mRPz9A7bagw8UtEWuZ0G47d93UmNb9XyO+fjtCopPZs2hSUE+VP8hqcedoWSUoRCVQ4gBrzJgxHDx4kD///JPatWvz+OOPk5SUxEMPPcT69esvZR2FEOKy5rCk8+f7fVk6qX+RICs35QC/fzCIF/9vHEf272Tr7HG4rFn0T9rAG9dvYnBrNSpndmDi+7X9XiMsJrnYnipJwSBE5VHqSe433XQT06dP58yZM7zzzjvs2bOHdu3a0aRJE959991LUUchhLjs+HNaAdhyUrBmHMeWncLSSf1JO7gRj9NK9qn9/GfCGN46MZDfMq7hjU9nYc9NJSSqCnc/+yXdh79FqwETguZXAdJTJcQVoNQBll9YWBgPPvggq1at4tdff+XMmTM8//zzZVk3IYS4LPlzWvknnIdEVSWxUSdUajW2rNMsef8upr3zPH1f+omZGV3JU8zEGOy0qKIEJq+HxiRjDI8Jmvju77WSniohKr8L3uzZZrPxww8/MHXqVFatWkWdOnUkwBJCXBXOzmnVss9Yrr/3XdZOG8HubX+x2Hobu7NaAmDAQc+aKYx+7glCQ0OL7D8If8+vOnuLGyFE5VXqAGvNmjVMmTKFH3/8EY/Hw91338348ePp2LHjpaifEEJcNgpv0Fw4E/vW2eOo3WEwWn0o65Vb2O1tCfi4VvcXAxrncNtjnwSCqnMFUOfa+kYIUTmVOMB6++23mTp1Kvv376d169ZMnDiRgQMHEh4efinrJ4QQl4XiNmhu2Wcsm2eNI/VMKqc/Hw6Kj056NznuEG7S/0Gi5gxkJ+HMz5IASoirTInnYE2cOJFbb72Vbdu2sX79eh5++GEJroQQV5zCk9eD/u5y4LCkYcs+zdbZ48hNOcBfB3N5bXsbPtvbALfDWpB93ZfDwPDvaVgjBpVWiz03tdjVhUKIK1uJe7BOnz6NTqe7lHURQogKVbiXqmnP59m35LNAj5WfLfs0Kfk63hoznV22WgAYVfFk2NSYVQrgI6lhR67tO45tP7/BqZ2LA6sLO4/8AXNSvQq6OiFEeSpxgCXBlRDiSld48vq2ueMBcNly2TRzNADZFiu/2bqz5FQSXjSo8XK9aQvdojahdbrxOlUYwmLRmyIwhMdy/b3vsuGbZzi1czFuWy6K4qvIyxNClKMLXkUohBBXmrMnr+tDzGh0RlL3r+G0K4YpWQPJ9xT8Z7Oedh89IpaRFGKl3QMfc/yvX7DlnEZvisBlyw1aXbjhm2cwhMUQFlOtgq9QCFFeJMASQlz1Cq8OPDvIcjvyURQv8bpsQjROwrUOepqXUld3GPARkVifYxtm0+z259HoQwCC9gzUGkK5/t53JQWDEFeZC040KoQQV4Kzk4ZCQU+WvtlQvjrYgJz044CKyJiqjKy/gcdj/kvLJA+JDW/ghoemEBaTjD03lR3zJgZee3YmdkkcKsTVp0Q9WBaLpcQFRkREXHBlhBDiUvI4rTjzs9DoQwJpE/zzrqyZJ9n47QvUuvXffLbwGD+uOIxPqYE5rAltdCuweL1o1UdRvB4cllTa3Pc+5qR6RCTWDcqH5U/hIIS4upUowIqMjESlUpWoQK/Xe1EVEkKIS8HjtLJl9quk7l1JVHJTWg2YEBgSbNRtJIs/vIflu7QsX/k7Dp8BUNHEuJ8m4afRq8ygUqHVmVDwERpdnT2LJgXlwyo8LCiEECUKsJYuXRr4+9GjRxk1ahRDhw6lXbt2AKxdu5bp06czYcKES1NLIYS4SB6XA5c1G5fdQur+1WyaOZpWAyagKApfTfuSb08OJNMbBUCS9gy3m5dTLzydsNiamCIaAuDIz0RnCEOjMxTpsZKtboQQhZUowOrUqVPg76+++irvvvsuAwcODBy74447aNasGZ9//jlDhgwp+1oKIUQpFSQJzUJrCAn0VLUaMIFNM0dzZu9KUvYsZ930kajUOhbur0GmN4oInYNuYSu4NmQ35oTahEa3Q63RBvJg+XupGnZ5hB3zJgb1WMmwoBCisFKvIly7di2TJ08ucrx169Y8+OCDZVIpIYS4UP55Vrt//4jUfcHDgQANuz7OoX3bcVlO4d61FK0hlJ6RR9iv78zttVJRO7wovnqotQXpGBp1Gxl4beFeKumxEkKcT6lXESYnJ/PFF18UOf7ll1+SnJxcJpUSQogLEVgROGc89twzuOy5pO5fw6aZo8lNOcDab//Nq29/zNun72eps3tB4k9FoXa8kT41j6JxZRMSVYXW90zAZE7AmZ/JnkWTglYXyspAIURJlLoH67333qNv37789ttvtGnTBoANGzZw4MABZs2aVeYVFEKIkvKvCHTZctGHmImt1ZqMI3+Rsmcli7Zl8VtOe3K8ZgDO+Kqg1ocDCtknd2LNPEFiwxuLTFyX1YFCiAtR6h6sHj16sH//fnr16kVWVhZZWVn06tWL/fv306NHj0tRRyGEKBF/YGQyJ+Cy5aLRGciJbM+naf34LvM2crxmIlQ59A35kUfjf6BKo/bE1WuLSqXGZc/F63YWW5asDhRClNYFZXJPTk7mjTfeKOu6CCFEqRTOwO7nD4w2zRzN0iNGvjrSAgAdLjoYltNev5KwsFCSGt1EqwEFK583fvs8aQfWknHkr8DqwsJZ3WWulRCitC4ok/vKlSu59957ad++PadOnQLg66+/ZtWqVWVaOSGEOJfiMrAX5nU7qeFYh0llp6VuM09HfUpn4zJ0ihONzkTTns8HgqjrBk0kseGN6E1m9CFRQb1VMtdKCHEhSh1gzZo1i+7du2Mymdi8eTNOZ0GXem5urvRqCSEumYK0C38HUv75VvbcVDbNHE1e+nHmrDjACx8tZtPM0aQf2oDGfpKRYf+hd8gsYsJVJDS4AWNELD6Pi53zJwZNXm81YAI3jZzJNX1floBKCHHRSh1gvfbaa0yePJkvvvgCnU4XON6hQwc2b95cppUTQggoCK42/TiGDd88ExQUtewzFn2ImbXbDnLnCzN4cfIK5q45zoqtx3HaclGhEBlmICQqifD42hjCoomr0waVWh1YXVi4vLDYZAmuhBBlotQB1r59++jYsWOR42azmZycnLKokxBCBLFmneT4X3M5tfOPoCDrZIaVj7bV4PPMwZx0xmNQu7nNvJLaxlOERlYhNCaZpEad6DzyB8JikgMT32NrtUZvikAfGiWT14UQl0SpJ7knJiZy8OBBatasGXR81apV1K5du6zqJYS4ShU3cR3U6ELMuLJOc3LHYpzTn2O94R6+XXYcj5KICh/X6jfRp24quvwjRNe4hlb9X0dRCGRyL7xfYIObH0GlAkNYtPRYCSEuiVIHWA899BBPPvkkU6ZMQaVScfr0adauXctzzz3HmDFjLkUdhRBXCf/EdbfdEpR3ypxUj/bDPmfJ+3fhcdlJ2fUHv9mb41EiqK05wG2hi2hcpwpaQyhKWF00OiMafUixqwtlRaAQojyUOsAaNWoUPp+PLl26YLPZ6NixIwaDgeeee46RI0deijoKIa5w/l4rIDBx3Z/cs+B5G7/Nm01C9WvIObYZr9vBrZof8Bi1NDQeJrbWNYTH16ZR1xHsWfzxOZODSqJQIUR5USmKolzIC10uFwcPHiQ/P5/GjRsTFhZW1nX7RxaLBbPZTG5uLhEREeX+/mXB5/ORlpZGfHw8avUFZc0QF0DavWIU1+5n91oBgQzq+hAzp3LVfLc3nl3WGgyqd5QOoVtIP7QBRVFQFC+G0CiqNO0SyF3lyMsMvN5kTpAM7Mj9XhGkzStGebR7SWOPUr/7sGHDyMvLQ6/X07hxY66//nrCwsKwWq0MGzbsoiothLj6FE63sHX2OKBgU2W3LpovN2h5edO17LLWQI1Crs1HxrEtoFajNYaCSoXLlovLbgmUJxnYhRCXg1IHWNOnT8dutxc5brfb+eqrr8qkUkKIq0fhgMiem8pfP43jq9+28+LaFqzOb4UPDU0jzjCmyXKut01D8XkJiUqiw/DJhMZUA7WGM3tXFpvCocWdo2W+lRCiQpR4DpbFYvlfl7xCXl4eRuPf/yv0er0sWLCA+Pj4S1JJIcSVrfAqv8+3xLAq7TCgI0GbQb9aB2kQcorMw3/hU6kIja5C55E/YE6qR3h8HZZO6o8tO4Uze5ZjzToVGA682ocFhRAVq8QBVmRkJCqVCpVKRf369Ys8r1KpGDduXJlWTghx5fM4rbiddkwRsTTqOoJOJ95ga0YUXUJX0r2JlmvufJG9iz4h58QOFBTaD/scc1I9oGB1YeeRP7B0Un90hnBCo5Iq+GqEEKJAiQOspUuXoigKN998M7NmzSI6OjrwnF6vp0aNGlSpUqVUb/7pp5/y6aefcvToUQCaNGnCyy+/zG233VaqcoQQlz+P04rLETy9IC09k1fe/wqNx8orj93B3sUfEW7ZxAux69FpVeSfMXJw5de07FOQAsaWc5rDq78mIqF2UAqHziN/wBAaiTEirtyvSwghilPiAKtTp04AHDlyhOrVq6NSqS76zatVq8abb75JvXr1UBSF6dOnc+edd7JlyxaaNGly0eULISpewR6CWexb8hkuex5VO47A5Y5hxuJdfDx7E/mOCNSE0vztwdSoUQO9KYKo5OrkpuzDY88nde9yGnZ5lFb3TAgkCj174rq/R0sIIS4Xpc6DtWTJEsLCwujXr1/Q8R9//BGbzcaQIUNKXFavXr2CHr/++ut8+umnrFu3TgIsIa4A/hQMDksaAA6rhVmz5zLveHVOpFsBqKJPp7v+F0J96dhytLS59z0Or/mWsNia2LJOEBpdnT2LJtGyz1hJFCqEqDRKHWBNmDCBzz77rMjx+Ph4Hn744VIFWIV5vV5+/PFHrFYr7dq1K/Ycp9OJ0+kMPLZYCpZm+3w+fD7fBb1vRfP5fCiKUmnrX1lJu5cPl8OOy56H02Yhh3im7mvEvpwIwIpZ56Rr2HKuMe1GbzCgKFUJja3Bnj8mAxAaU51W97zF3sUfozWFo9bqA4GVfG6lI/d7+ZM2rxjl0e4lLbvUAdbx48epVatWkeM1atTg+PHjpS2OHTt20K5dOxwOB2FhYcyZM4fGjRsXe+6ECROKnUifnp6Ow+Eo9XtfDnw+H7m5uSiKIsnoypG0e/mp2nEER9Z8j5KTx7H8MHRqhRtjDtExZh9GnZrIqndSq+0AnPlZHN/0M57/vS7+ukE41RFU7TgCjVZPVq4VsFbkpVRacr+XP2nzilEe7Z6Xl1ei80odYMXHx7N9+/Yimz1v27aNmJjSL4tu0KABW7duJTc3l59++okhQ4awfPnyYoOs0aNH88wzzwQeWywWkpOTiYuLq9SZ3FUqFXFxcfJLWI6k3S9ewfY2TrR6Ax6XE2P43wtfHHlZeNCweOMxenWoQ+ztj7Ft7niG1dlOXLgaw8lFqPN1RCTW45ou92IIi2Xb2i/R2lMDZaRt/JoWvcdgjK9bEZd3RZH7vfxJm1eM8mj3wmmqzqfUAdbAgQN54oknCA8Pp2PHjgAsX76cJ598knvuuae0xaHX66lbt+Af0FatWrFx40Y++OCDYochDQYDBoOhyHG1Wl2pb2CVSlXpr6Eykna/cB6nlS2zXsGecwZTZCKK1x3YksZuyeCT999k1rHaZDoM5OyZz5CHn6Jx18fI/340eU4VPnyEx1ZHo9Wy+7d3AXDZcgkxJxTaT/AM2+e+KlvdlBG538uftHnFuNTtXtJySx1gjR8/nqNHj9KlSxe02oKX+3w+7r//ft54443SFleEz+cLmmclhLi8eJxW0g5t5NjG2XjdDowR8UTE1ynY/696X976biOH8xsBEKWz4XHasGadZueCd8g7cwAlojZqrR5DeDSgInX/GkAhoX6HQDDlTzp6rk2bhRDiclfqAEuv1zNz5kzGjx/Ptm3bMJlMNGvWjBo1apT6zUePHs1tt91G9erVycvL49tvv2XZsmX8/vvvpS5LCHHpFfRcvcqpnX/g9bhQfD4cljRyPGEsPtiUv34/CEShw8mNhpX0bWXk+r7/Zuf8iaTuXw1oMFdpiDExEbctB60hFK0hBI/TFvQ+hYMs2U9QCFEZlTrA8qtfv36xGd1LIy0tjfvvv5+UlBTMZjPNmzfn999/p2vXrhdVrhCi7BTMtXJgDI/B43LgsmXj8zjQGcJwOfLwejz890wP0nyJgI8Wmr+42bCIhi3acm3ff7Nn0SQc+ZnoTWYik5tRvfNwYqOj2T73Vey5qURVa4ri82KMiA8KpPxBlqRlEEJURiUKsJ555hnGjx9PaGho0CTz4rz77rslfvP//ve/JT5XCFH+/Hms3HZLYJiu1YAJbPx+NGf2rkSlKKjwcZP2N9Z5OtJd/zNJmhRia1/PtX1fITS6CjpTBKFAm3vfR60zYbF7MIZHB/VQNezyCIaw6CKBlAwLCiEqqxIFWFu2bMHtdgf+fi5lkd1dCFEx/D1VWr0x8Kc16zRuuwVr5kk2/ziGJrc9zZ4MLW/vu4lGDjstlUUoipcGmp000OxEpQJ9SDQ6Qwh7Fn9Myz5jaXHn6EAPmM/nw2IvSDoqPVRCiCtZiQKspUuXFvt3IcSVwd9T5czPQqM34XXZ0OhDULxuane4j3XT/sWRU6m8s2YqO2x1AEjTtKR5yGJQFAr+b6UClQqvy4Yl7RDAP05Qlx4qIcSV6oLnYAkhrgwepzXQU2XPOYM16zjGiAQcllT0obEs+fIJVtg6szSjLl60qPBxnXEzHdULwPt3gl+1Vg9qDYrPG9gaB/45yBJCiCtRiQKsPn36lLjA2bNnX3BlhBDly2FJZ9svb6J43TTqNpI9iyYBYM06jloXwpoD+fzivAebEgZAHe1Bbgv/k1jfMXzugnQqKrUWY0QcoKD4FNzOfBSvB5c1G40hRFYBCiGuSiUKsMxmc+DviqIwZ84czGYzrVu3BmDTpk3k5OSUKhATQlQsj9PKtl/eJHXvckKik9mzaFIgyPI4rWQc+YsIkrApIcRqMuhuWkhD41F8HgeKogAq1HoTNz3+PaFRSYFUDDrCcDvzCYmqxjV3jSE0pprMsRJCXHVKFGBNnTo18PcXX3yR/v37M3nyZDQaDVCwUfNjjz1WaberEeJqcXbKBcXrJjS6Otas4/g8Lv786WPSQttgPjkLVGoSNSk8lLiAJNt69AYDWr2JyLptyDi4DnWIgZha1xKd3CSwunDTzNFkn9hJYuNOtLzzJcLiSp8fTwghrgQqpeC/oiUWFxfHqlWraNCgQdDxffv20b59ezIzM8u0gudjsVgwm83k5uZW2uDO5/ORlpZGfHy8bKdQjq7Gdi8u5YIjL5Ots8dx5vQJ5hyIZb3jOgAeC32PGFUaGkMIXqcNlUqNSqsntsY1hMZWp1a7ezi8+htctlxM5oSg8rwuW7EpF+DqbPfLgbR7+ZM2rxjl0e4ljT1K/e4ej4e9e/cWOb537158Pl9pixNClBOPy1Ewkf1/28848jLRmKLYZurDK7tuYa2jLT401NXsR614iEhoAErBHCsFMCfVx245gz3nDMc2/ETTns9jMicEzbEyhscQGpMsQ4JCiKteqVcRPvDAAwwfPpxDhw5x/fXXA7B+/XrefPNNHnjggTKvoBDi4hQeFvQn97TlpDLts3f56XhDjme4ACPx6hS6G+ZTS7MPnT4MW85JIqs2wZmXhjE8Dqc1E2NEAtas4wCBOVuh0VUkoBJCiLOUOsD6z3/+Q2JiIu+88w4pKSkAJCUl8fzzz/Pss8+WeQWFEBeuuGHBln3Gsnrma3yyLBmHz0Wo2sbNxj+5RrsBjRr0png8TitaQzguawbthn3G4dVfo9Lo8LpsmKs0xOuyF2Rol+BKCCGKVeoAS61W88ILL/DCCy9gsVgAKu38JyGuRB6n9X8JQ0MAAsOCa2aOp1XPEWh0Rhq3702XTV9icelpr15ImEmHMSyJVgMmcHzTXKyZx7Flp2CMSODw6q8DPVVnZ3qX4EoIIYp3QYlGPR4Py5Yt49ChQwwaNAiA06dPExERQVhYWJlWUAhRch6nlS2zXyV170qikpvSasAEGvf6P/7z4WfM3RjHvbuepb7xOM78bNrpNXhVDlB8oGhoN2wy8XWvI7Z2K7bOHodao8eadRxzlYaBnip/QCWBlRBCnF+pA6xjx45x6623cvz4cZxOJ127diU8PJy33noLp9PJ5MmTL0U9hRDnkJ9+DFtuGvF1r8PjcuCyZuOyWzizbzWT3h7Dguz2nMqqAsD6zBpUNawGjZaQqESSanUm6+hWQiKTOLz6ayISagfN1QqLr0WLO0ZJQCWEEKVU6gDrySefpHXr1mzbto2YmL+3vrjrrrt46KGHyrRyQojzy08/xsIJt+B1O+n85Czi615HqwETmPXleL7aauaIuzrgxKxzcIvpT5p4VoBGizEsmg7DPie6elOsWafZs2hSYHVh4blaMgwohBAXptQB1sqVK1mzZg16vT7oeM2aNTl16lSZVUwI8TeHJR2nNQdzUr2g47bcNNxOG16Xk6Uf9KXzk7P4bouaTzY2QwG0uGmnXUan8I3ofHYwGNCbIjBXacjhNTOISByLOaleoMfq7JQLQgghLkyp82D5fD68Xm+R4ydPniQ8PLxMKiWE+JvDks6SD/qx/OOB5KYcCHrOEBqJITQK8OF22Fj6QV/C87aiAM20W3g85G26mdeg9VrRaA0kNerEjY9MIywmOSgflr/HqsWdo6XHSgghykCpA6xu3brx/vvvBx6rVCry8/MZO3YsPXr0KMu6CSEApzUHjzMflzU3KMjKTTnAso8GssVSm/2Gzmj0BrwuB4Z1o3nU9D59TN8TF6bC63GBz4fX60Kt0RGRWJeWfcZiMicUCbIkuBJCiLJR6gDrP//5D6tXr6Zx48Y4HA4GDRoUGB586623LkUdhbiqmZPq0WnEd+hDzYEg6+S2hXz1zlN8ePIOZlru5DfHHdTv9er/XqEQrz6J1hCGRmcgNKoqpqhE9KYIMo78xaaZowECQVbhYUEhhBBlo9R7EUJBmoaZM2eybds28vPzufbaaxk8eDAmk+lS1PGcZC9CcaEux3b3Z1z355kqPAfKkZeJLfsUKyYPIT0fFuV3ZpujCQAmvZrBN8RRZesTqFx5KIoHFAVQkdikM+3u/wCPy8HO+RNJ3b8GUEio34FWAyYAlOtE9sux3a8G0u7lT9q8YlxOexGWapK72+2mYcOGzJs3j8GDBzN48OCLrqgQ4u+M6wUJQk0oXneRDZmdip6ljq4szqiNBx0qFLo3C+XR7jXYMfUevG4Har2RmBotSd2/GhSFjEMbsGafCawu3DRzNNkndqIPiZIVgkIIcQmVKsDS6XQ4HI5LVRchrlqBjZhzzmDNOk5IdDJbZ4+jUbeR7Fk0CWvmSfaczGXhmXtQUFNLd4we4UtIzs5h23+zUTxuNHojnZ+cRXRyY07tXMraqY/ic7sCqwv9QZbHacMYHi3BlRBCXEKl7j8bMWIEb731Fh6P51LUR4irgsdpxZGXGXjsX8UXGlMNkzkRa8ZxLKmHmPP+w+SlHsaSdpAYx246GpZxX9wCvny6HbUiHXicNnwuByqtLhBEaQ2h1Gh1Ozc/NQeN3ohGZyDEHB94n7DYZAmuhBDiEit1HqyNGzfy559/smjRIpo1a0ZoaPA/1LNnzy6zyglxJSq8AXPTns8DoDWEYAyPoVG3kSz/eCDpTiML9jZhj6MuT9i+JNKbjqIo9EzaTeeRP2BOqkdEQl2Wfzww8HpDaGTQ+8TXvY7OT84ixBxPWFyN8r5MIYS4qpU6wIqMjKRv376Xoi5CXBX8w4HW7FMs/aAfCj5iarSkac/n2TT/Yxbmd2FpWm28aFHh47C7Bq20KRjDogPBFfy9unD5xwPRGsKKBFhQEGQJIYQof6UOsKZOnXop6iHEVcM/HLhp5mhyTu7CZc/DbrXwy1Ybiy3tyPcUpEyord5PN8MvJKlziKzSlJCoKhjCooPK8gdZhtBIjBFxFXE5QgghilHiOVg+n4+33nqLDh06cN111zFq1CjsdvulrJsQV4zi5ly1GjCBxIYd0epD+Sy9P3OyOpPvMRKryWSgcQr3hU6liimP6OTmuGwFGzj7k4IWZk6qJ8GVEEJcZkocYL3++uv8+9//JiwsjKpVq/LBBx8wYsSIS1k3ISolfzBV+M9tP09g6+xx5KYcCBzX6o3U6zQctUZDA90+TFi5Vf8zjxjfpoH+IKExVbn5qTmYqzQgNLo6tqwT5GeeKDbIEkIIcXkp8RDhV199xSeffMIjjzwCwB9//EHPnj358ssvJYmaEBTsGWjLSeHQqhk48tLRh0aheN006jYSa+YJHHmZLJ3UH2O1tvx6LJlr4izEZS1G8XroaN5OK+06jEoeao0OfWgk7YdNJr7udUQk1Gbr7HGExdfC67JJ5nUhhKgEShxgHT9+PGivwVtuuQWVSsXp06epVq3aJamcEJWFf0NmlzUHNBq8TisavYnw2Fpsm/saGUc2Yc3JYKO7LcuONsZBCNtPw+PRORhCI4iMqwUH1qF4NaAqKPPAsv8SkVA3MGfLn+FdEoQKIcTlr8QBlsfjwWgM/l+zTqfD7XaXeaWEqCz829v4N2R2O/Lwepxo9aF4HFZyUvbizMtmr7M2i12DyVQK8lElqM/QI3wJhtAIzEkNyD6xA32IGbVWh9ftwO3I58zelWyaOZpWAyYEts2RwEoIISqHEgdYiqIwdOhQDAZD4JjD4eDRRx8NyoUlebDE1cDjtJKfeZJ9Sz4PbGvjT5ngyMvE7chD8Xk544pikes+DvsaABCqttItfBXXh+8Fn5vw2EZkn9gBqEhseCNNez7PzvkTyTy6FY8zn9T9a4oEWUIIIS5/JQ6whgwZUuTYvffeW6aVEaIycFjS2TJ7POkH1+HzuQmPrcXW2eNo2Wcs1/Z/kxWT78fnsgMKGb54DvsaoMFDh5CNdI7YSHxiMhp9fbweN7bs02j1ocTUbBkIoloNmIDXZWPrnPEF+waGRsmcKyGEqGRKHGBJ/itxNfIPARae/7TtlzdJ3b8Sj8OGSgWWtEP4fF42fPMMZw5vIcURTYL6JACNNNvpqP+TltpNxOgsaH0h2C1naDv0Yw6vnoFGq0NnMtO05/OBHqqCP/8OtAxhsm+gEEJUNqVONCrE1cKfXsGZn4lGHxJYEah43YRGVSXz2FZ8Xh9gwZGXyS5nAxbbB+NUDDxuehOjyoFKBTdpfwOVGkXR4PU4MUUkcnj1jMBGzvbcVPYsmkTLPmODhgH9gZYQQojKR/IrCHEO/i1tbDlnSN27HGvmSfYsmkTtDvdhzz2DWqND8bk47U7gv7n384NtENlKLGqVj0xfPGqtAZVag0qjRaVWoVJp0OpDsOacCpTVqNtITOYESb0ghBBXGOnBEuIctHpjoJcJID/jGG6nlXXT/oXWEE5ulo0/XL3Y4mwGqNHiop12GR10yzDqVYREV8OcWI/clH24rLl4PU5AhVqtQx8ahc4UQWh0lUAKBhkGFEKIK4cEWEJwjrlWP0/AbbdQu8N97F/6OZbT+8g+sQONzkBeZg4fWp7BqRSsqm2m2cTNugWY1Ra0hhC0xlBiqrek+R2jAFj+8UDUWiPh8XUIiUqkcbfHA3OrJLASQogrjwRY4qr391yrLDR6U2CuldtuwZp5knXT/kVoXG0cthwUrwev20GYIZSGmu1k+uLopvuZatqTqLUGdPooVFotGp0RlzU7MLfKvyEzKrX0VgkhxFVAAixxVfM4rdhzzmDPTcWacRxHXjqmyCT2LJpEtWvuYNP3L+LIS2dXio8lruH00n1HtLogz1UP3Sy0uNHoDJgiqxEWUx1r9im8Ljs+j4u8jCOExddCqzdiTKpX0ZcqhBCiHEmAJa46HqcVl8OO1+1g+68f4chNI/vETmxZJ9HoTDgs2/G4bBz7ay45SjQLc+9gl/caAJapbqOP4RtQVOhUblRqHabIBDo8+F8Or/4atUZPXsYR1GodcfXa0uKOUdJbJYQQVyEJsMRVxT8c6LLnEX/dENz2POzZp7FlnwKVCkd+Oiq1ltRje1np6sw6d0c86AAfLbV/cUvIUuJqtSPr2FZUai2Kz43JnMTh1V8HJsSHxdeiYZeHCY2uJsGVEEJcpSTAElcVf+oFe24aJ7fMo3nXEexb/BE+r5uMo5tRqdRsdzZlsfsO8pUIAGqoD9LduICqhgyiqzXFkXuG6ORmWLNPoUKFMy8DlaaprAgUQggRIHmwxBXP47TiyMsECpJ3tuwzFpM5Aacth10L3qF66744rZloDSEoPh85Sgz5SgRRqgz666dyv/FzqhmzuLbfazitmbjteTitmVx/77skNLyR0NjqeF02PC4HxvAYCa6EEEJUbIA1YcIErrvuOsLDw4mPj6d3797s27evIqskrhD+oMo/JLh19rhAkAXQpMezaA0hpO1fw29TxpBKLRSvG60hhLbaZdymn8O/jG/TULcHtRrCYmuy74+PMUUkog81ExpdnZNb5tHktqcJi0nGmZ9V5D2EEEJcvSo0wFq+fDkjRoxg3bp1LF68GLfbTbdu3bBarRVZLVGJOSzpZB3fzqYfX2bDN89gzTr9vyHBVDZ88wypB9az4Ztn2PHr2+TbXMzPas9/Ugbz1dGW+NDgcdrQqbxcp12FVq2A4kNjCMGacRSPw/a/fQQ/JTSmWlCiUMnGLoQQorAKnYO1cOHCoMfTpk0jPj6eTZs20bFjxwqqlaisHJZ0lnzQD6c1G5ctG8XrAaBF7/9j29zXOLVjESe3LcCraNjsvp5l7luxegsShYapLOQ7fYRptPh8HrSGcLxuB1pdQeJRY1gMap2B2NptiE5uTEShuVZaQ6jMvRJCCBHksprknpubC0B0dHSxzzudTpxOZ+CxxWIBwOfz4fP5Ln0FLwGfz4eiKJW2/pcDj9OKIz8be84Z3E4rbkc+HpcDUHNq5x/YLBlYM47j9bg55K7LIlcv0pVEAOJ12dyZuI2knEWoUFBUaiKrtcDrtmIIjSE3ZS86Yzgel53Q2JoogMthxxhecI/6Pzd9aFTQY1E8ud8rhrR7+ZM2rxjl0e4lLfuyCbB8Ph9PPfUUHTp0oGnTpsWeM2HCBMaNG1fkeHp6Og6H41JX8ZLw+Xzk5uaiKApqtaw5KC2XLZfDa2diObOfkKiqNOj7Lvv++AS1PQ+Py4ai+Mi2WEEfy/Gwxsw40w0Ak8ZFj/oObqntROWpQX5GN5x5GWj0Jrw6Aw1ueYzU3UsxVGuLJWUfaq0efUwySqiZbEs+Grungq+8cpL7vWJIu5c/afOKUR7tnpeXV6LzLpsAa8SIEezcuZNVq1ad85zRo0fzzDPPBB5bLBaSk5OJi4sjIiKiPKpZ5nw+HyqViri4OPklLAWP04o16xSHln9E9r7VOPIzsB1R0DlTubbbMDZ+8wye3BQUn4JKpQCQrEBNbV2qGnLoYv6LpBpdad3tIXT6UFB85KUf5eDKaWj0Jmo3bEn12o3Q6g1Ys1MwhET+b5sbgwwDXgS53yuGtHv5kzavGOXR7kZjyebaXhYB1uOPP868efNYsWIF1apVO+d5BoMBg8FQ5Lhara7UN7BKpar011CePE4r2+a8Rsrupfh8bkLM8Tjz0/F53Jze8TvpB9dizclmo6cdmzztGW78AKPKgQoVD8XN4Ybhn3JojQeb08buBe9yTZ+XMYbHExIZjzmxTmAuld4UDhD4U5QNud8rhrR7+ZM2rxiXut1LWm6FfuqKovD4448zZ84clixZQq1atSqyOuIy5XFasWaeID/jREHqBZcDly0bj8uKx2HDmnOa8IS6oNbgcTnYlhnDp45nWOTuTaYSz2ZP27/LsmdzYPkUmvR4FkNIJPbc1KD0CpLHSgghRFmo0B6sESNG8O233/Lzzz8THh7OmTNnADCbzZhMpoqsmrhMeJxWtsx+lTO7l6PgI6ZGS1oNmECrARPY8M0znNqxCG+eC3t2CqlKVX533MoRX30AQsmjs/43Wmo3otboCiY+er2c3rkYBRXVOj1B2sZMdKZwSa8ghBCiTFVogPXpp58CcNNNNwUdnzp1KkOHDi3/ConLSsE8q9O4rNm47Lm47Lm47Xlsmjmaejc9hM/nRVEUFJ/CfPddbPa0QUGNBg9ttCu4UfcHIQY11w36mJ0L3sZlzcHtyMPr8ZC6dwVJHR6lRe8x6I0m6bUSQghRpio0wFIUpSLfXlzGPE4rW2a9isuWTdOezwNwZu9KXPZcUnYv5fiW+eDzoQLUGjVulw4FNY01W+mim0+UOqugIMXE6R0LaT/sc9ZN+xcmcyJuVz56UxTGsCiM4dEyP0IIIUSZuywmuQsBBUGVfz8/R14WqfsKAiqgoMfK6yV13yrslnT2eJqSqD5NrC4XxefhZt18rtGuo4bmCGqdiYjE5uSlHkLxeTi9czEAbYd+SkR8TZzWHHQmMxaH5KcRQghxaUiAJS4L/j0D3XYLTXs+j9ftICq5Kan713Bm70rSD28kskpjTjhjmO+4m+O+2jTUbKe/5msAItR5RGqdoDahN0bgceYRndyU7FO7QFHITdlHSGQixog4jBFx+Hw+LI60Cr5qIYQQVyoJsMRlweNy4LZbsGafYtmkAUQlN/1foPUap3cuJt3iZsYpM9vcwwHQ4iJedQbFp6A1hpDQsBNarR6AjCN/4XU5sOelUbV5dxSPB1NkYiD7uhBCCHGpSYAlyp3HacWZn4WigNYQgjE8BmN4DC37jGXDN8+QnZ9Byp7luB1W3OhZ7urGCmsb3BQEUM00m+gatoQwTzoq1OgMoej0ITS/YxSGsGg2zRxN9omdxNRuzTV3jflfglDZJ1AIIUT5kQBLlBuP04ojL4s9iz8iZedSFLzE1mpFqwETAPC6bAC4HVa8bgcpu5aw1tuZP61dAKimPkJ33c/UjfHQ4cHP2fvn52Qc2ojbmUfagTXsnD8xkMLB67JhCIuWoEoIIUSFkABLlAv/HCtb9mlc9jysOafwuR24Hfls+KZg+6OskztxW3NxOr1oVQoKCtcoy9itrsV12tU01u5Aq9MDZg6t+oZr+oxl5/yJZB7dikqlRh8aVainKqZCr1cIIcTVTQIsUS6c+VnkZxwn8+gWVGo1htAo7JZ0nHkZnNy2EIAsVyh/unuQqcTzSNhkNDoDOk8uQ02fYgiNpmXfD0jZuZiMI5tI3b8agKY9n0ejM6JSIT1WQgghLhuSAEhcch6nld2/f0T2qV0FW9w48lGpNRjDolF8Puxu+MN2Mx87XmCX9xpSfUlYEruD4gGVCrVGh1qrI23fClr0/j8S6rcHVGSf2IlWbyQsNpnQmGQJroQQQlw2pAdLlCn/PCv/5HUgsHeg12lDozHicdsB8CkqtniuY6nrNvKJAKCm5hCDGpymTpyaDHsU+tBoIqs2JPvETlL3rwEm/i/x6ET0oVEYwmRloBBCiMuPBFiizDgs6WyZM57Mw38RldyUVgMmBFYI1rvpIVIPrMFlSUet0ZFtU/jGNoQzvqoARKvSuUU3j0aGA4TkJ+IMqU1Cgw40ue2ZoJWBzvws9iyaRNOezxMaXUV6rYQQQlyWJMASF83fa7Vr4fuk7luJ1+Ugdf8aNs0cTdOez+Nx2Vkz5WFc+dmoNFp8Hhcmnx0NbgzY6ahbRBvDBjRqL3h9OCzpqNRqrhv4NuakegCBlYE75k1EZ4qQ4EoIIcRlTQIscVH8qwMdloKs6OGxtcjLOILX5eDM3pWk7l+D25ZLvkfNWmdnOkduQ8k/jUoFvfXfYVLZCVHbMUUkoFJrsFvS8XmceBz5gbQL/l4wKMiVJTmthBBCXO4kwBIXLD/9GDkpB3DbLbhsuehDzBgjYgHIPrkLpy0br8fHJk8blru7Y1NCwaLnJtUcAGLUGaBSo9EZ8XlcaI1hmCLicFqz8bpdaHQmtHpj0Hv653UJIYQQlzMJsESp+IcDndZMln7QD6/bSYeHpnByyy9YM0+i0YfgdTtwWNI46K3PYtcdpCuJAMRrM0lW9qDW6VBr9GiNoaAogAqPy4pWCSGmRksUBQzh0bS86/+kp0oIIUSlJAGWKDGP08qWWa+Sum8lxshEPC47PreL1V8M47p732f7z+OxZZ8h1RnOYtdwDvoaARCisnGT8U9a6zagUYMpsio3PjIdrd7EzvkTSd2/Gi2hqFQajOYEmtz6FMZwyWklhBCi8pIAS5SIw5KOJe0oLls2LnsuLruF2DrXk3FoAx6njbX/fbhgqM9tZ7X7Tg76GqHGQ/uQLdwc+Rca22l8HhfGiCTaD/ucmBrNgYLJ64X3DmxxxyiMEXEVfLVCCCHExZEAS/yj/PRjLPt4MD6Pg7ZDPwUgdf9qLCn7CY+rTcbxXTgxEOLLB+Bm3W940NMjdjMN69fBba+OJc2D12XD5/NwYNkXRCTUDkxel70DhRBCXGkkwBLn5J9vtf3Xt7FmHket0bFu2r9oO/RTvG4nKXtWsSXNx2L38ySoT9PP8BUAkXoHQ+KXEx5fG43OgEZnACAv4whqjS6wZ6Cff4WgEEIIcaWQAEsABcGUx+UAxYc1OwVDaBT7lnyGw5KG1+0kqlpTso5vx25JZ82XD5Ghqc0PWf046ivIU+X0GrApoYSo7ag0WlrcNZa0vcuw56YGVheGxdei4c0PExpTTXqqhBBCXNEkwBIFGdhnjyc/6yT5aYdwWbOJr98evSkCW/Zp8tOP4fW48Lgd5DuMLM25ni2e6wA1Gty01S7nBt0S4pPrYk0/is/jZuM3TwVWF+pMETTs8ogMAQohhLhqSIB1lfM4rWz+aSxHNsxB8dj/d1RFyp4VxNa6lsyjW/A6rQAc99bkW+dDuCgY3mus2cotunlEafPQmcy4bbnE1m1DxqENeF0OVn8xjA4PTSGxQTsJrIQQQlxVJMC6ylmzTnJm38pCwRWAgs9lI23fqqBzk9SnMKocxKrS6Kb7heqaI6BS0Xrw+2QcWB2Y+O5fXajRGYhMqifBlRBCiKuOBFhXMY/Tyt4/P0fxeop9/pQ3mS3eNvTUzUKlUtCp3AwxfEykKgdDmBmvOwS1Rsu+xZMCqwuzT+zEnFiPJrc+RXhsDcLiapTnJQkhhBCXBQmwrgL+CexavRGPyxHYbsbjcuBx5GFOqo/i8+GwpAJg8ZlZ4u7Bdm9rAKqpj9FSuxGAaG0usXXaFmxpk5dJ1ontOPOzA6sLQ6MSZa6VEEKIq54EWFc4/2bM9txU1Fo9arWGln3GBu3pl591EhXg1Uawyt6W1e7OeNAD0FzzF7XV+wFQqTUkNOqERqNDozMQEl0FgKwT21FrjUTE15QkoUIIIQQSYF3xHHlZ2LJPk3FoIy57LtHVm7N19jgadRvJzvkTyc84ji0nje3OJiy2dyVPiQQgWX2Ebrqfqao5EShLUWnQaPXoTRGBzZ1DoqsQGluD5r1ekOBKCCGE+B8JsK5gHqeVPYs/wuOyo9JqUat1ZB3fjtfjZumk/rhtFlx2C2pdCOsd15OnRGJWZXGLbh6NNdtQqc4q0OsmZc8Kkhp1/F9uq3hJvyCEEEIUQwKsK5THaSX3zCFS967EZbcQldyM3JS9uK0Wso5vI9sTgdGXj0Htxedx0l03l6O+urTVrkCrKn7SOyj4PC4ModG0uHO0BFZCCCHEOUiAdQXxb20DBLKwRyTWI+PIX2Qd20p4XC3O5DtYamvDOndH2mmXc0vIEhRFoZrmONU0xwHQhUYRmVif/Mzj2HNSAuWrNFqqtejBtXePk+FAIYQQ4jwkwLpCeJxWtsx6ldR9K4lIrIdGZwjMk4pKbsbpXStYneFlqfthrEoEAKm+Kmj0oXg9dhSvGrVWT8u7x1O1yU3sWTQp0DvlsKSjMYTQYfhn/9/enUdXVd7/Hn/vM2c4STgZgJAwD0ECYVSRKhFUZCoIt6QULJbcqv1REK1dvWltEadovd5KldpiEZb+igK9ggpF5FImQQQCEVBkkkkgAUJIToaTkzPcPyjRgKWgIZskn9daZ7nOsPf+7u9C1odnP/vZtOx6m0atRERE/gMFrAbuwhIMgaoKKksK8FeWcOZQHgnt+mC1u6g4V8iOI5W85/0ZheHzd/15jNPc6VjGDVFHCVb5cETF0azzALoMepCUHncA0HPMDPLfnkl0UjuSuw+hWXIXrWklIiJyhRSwGpgLgcrljq9ZgqGqrAirIxIAT+uenD2az+mDWzAsVtacSWOl7/sAuKjgNvsq+tk/onnH3vhK4/GXlxDwVxCq9vHljndJaNcLlzselzuenmNmYHO4NGIlIiJylRSwGpALgaq6spSeY2YAUF1ZSsW5AsrOHCbo92GPcBObfAOn9m8kVF1NGmWs4XYybNsYaP+AaHs1zigPFWeP10x8DwcCVBSfJKZlF2wOV83xvr5WloiIiFw5i9kFyJUL+H1UlRXhPX2YvIU5wPlLeRExiQT9lVSWl7LuVFvmf9oCi2EjHAoQaznHQxFPMdT5DtGOEM07f4/EjjcDYYqP7SK2ZRoJHfrR4oZMMr7/vzRaJSIiUgc0gtWA2BwuLFYHxUd3Ulqwn7yFOaQP+wUAe33tWea9lTOhJAC6BdeTYikFIMKoJCKmBZ62PSEcxmp3ktCuL2cObaP8zBH6T/4zsS06KFyJiIjUEQWs69DF86x83rPYnOfnWPkrzmG1u/BXlnDys3UcOF7C2ycz2FN6EwARlJNpf59ky3EMqx2L1U44HALDwGpz1rq7MKFdXyJiWyhciYiI1DEFrOvMVxPXz9Jl8IMcWD+fwn0fEt+mJ+nDf4nV7sTmiqKi2sJ7Z/qTV3ATYaxYCHCj7UNus6/CZVRhWKw4oz0ktOtLOByk+NiumrsLtQq7iIjItaWAdZ05P8/qLIV7P6Tw8w1gteIrPYW/ooTTX2wlKq4VgaoKApXF7A12I4yVLtZd3GlfRrztHBabHcMSRUzLNPzlRQT9lbhiEkho15fSgv1ExLXghrt+rmAlIiJyDSlgmSxQVY6/shQ4P3fK5nDRrv8P+TL/HwT8FRhWO1abC1/pGb4Idab92e0Q9GEPhxjuWIwdP+2sB8Gw4IjykNjxZm4Y8hBfbHyDsqJjlJ89vzq7KyaBW7L/TExzXQ4UERG51hSwTBSoKid/yVOUBewkeKbjcEXwyTu5+EpP4WnXm4LP1kGwmhPVSaz0ZXE42JFRwTfJsG0DoLNt77/mWDkgHCJQVY7FYiWmefuahULdSe0J+itxRnsUrkREROqJApaJfN7zlwKrXS3Yvvg3dB/+KNWVpfhKz+A9dYByI45/Vg5mR7AfYMFKNRXh85PdMSw4o+NJ7NCPcChE0ZEd+CtLOXd8D4GqCqITUmsWCg34fVowVEREpB4pYJnI5oykWWo6p04XcWrfJnbzPJ0GZrNu7hT+WZjGhqpM/DgB6GbdwWD7cuIsxV/bPoJeY2fijPaQtzCHgs83UF1VxidLn6RPVm7NQqEKViIiIvXL1IVG169fz8iRI0lOTsYwDJYuXWpmOdfc+SUXimr+63LH0/sHTxPXKo1wKETB3g1s//vvWFA4iNVVQ/DjpJXlGD9xvsRY539/Fa4MKwDB6ip2L38egD5ZubRIuxVnZByOyGa1VmQXERGR+mXqCFZ5eTkZGRlMnjyZMWPGmFnKNfHNzw08i9URQcDnJWP0b4n0tKJN3zGc+3Q5VaUn8ZUWcaPlNMcsLbnD+T7djG0YBP+1RwMsVqw2B1Z7BNU+LwWfbyBvYQ59snLpk5VLoKoCl1t3CIqIiJjJ1IA1dOhQhg4damYJ18y/e25g5bkCvGcOEfRXUvxSFu3HzCL3jU1ElfTiexzFMAxSLYeZ6srFZg0TDgYBA6szkvi2vSg6vAODMDZXJJZqG0F/JVZ7xFdzrPT8QBEREdM1qDlYVVVVVFVV1bwvLT3/KJhQKEQoFDKrrG/k91Xir/RSWXKKHW8/Qcbo39Jj9O/Yvvg3nDuxl7JKP6tKu7Lh93lUh6Ox04++MWuIi3Tg93mhuopw2CCyWWsMwyAqsTWRzZKxudwUH92F1R6Jp3VPHNEeeoz6DRZ7xHXXg+tZKBQiHA6rZ/VMfTeH+l7/1HNz1Effr3TfDSpg5ebmMnPmzEs+P336ND6fz4SKvlmw2oe/0kt8r/F8mb8Mb1UFW5f9iZRew6gw4thu3M5KbxqlgQgA2kSVcnezzcTH9yMqPoWyU4fwV5RisVhxt84gtc/3KfxsDRUV57DFJhLbtTXO6Gak9hyOPcJNqS9Eqe+UyWfdsIRCIUpKSgiHw1gseuZ5fVHfzaG+1z/13Bz10Xev13tFv2tQASsnJ4dHHnmk5n1paSmpqakkJiYSExNjYmUX5ltVYXM4yV/yBwr2rCdMiLjkrliBwOljbFxykHn7OnO8ujMAzaznuMPxD9JbWHBUFnDTiD9y4tPVRJJEoCqaokPb8R0rp9XoqbRu/198svRJ7C7ofPsUXNHNNM/qOwiFQhiGQWJiov7yq0fquznU9/qnnpujPvrucl3ZTWQNKmA5nU6cTucln1ssFlP+AF+YxG5zuNj13nNUV5bS9a6pVFcU4y8/S7XPS3XFOSxWO4RD+P3RFFb3x4GP25xruNn1MTYjiNWSTnRSG45uW0L68F/WPNg5b2EOjqhmRLjjsTmj6DXmd1rPqg4ZhmHan52mTH03h/pe/9Rzc1zrvl/pfhtUwLqefH0Se9e7pp6fwF5SyJ4PXqJT5k85/cVWSiv85J9NobttBwYGLuMUYx3/TSvbUeKcIeJSbqDKexZbQlt8hTuw2uzsXv48PcfMwOWOp09Wbq1A5dIEdhERkQbB1FhdVlZGfn4++fn5ABw6dIj8/HyOHj1qZllXJOD31QpVXe+aSkRsc8qLvmTTvP9ie/VNvOSdzpKqH3Es0JpwOAThEF3sn+G2lGNzRuJObMttP19AtCeFKE9rKs4eo6zoGPlvz6xZJ0ujVSIiIg2PqSNY27Zt4/bbb695f2F+1aRJk5g/f75JVf175xcIPUuwugKw1Dzvr7KkkN3Lnyel1wgWvv4Ky4rvoSDQHIB4y2nCFgeGxYphsWKPcGNzRhMOBQBwRnlod8sPOb6+CHdSW4L+CuwRMVooVEREpAEzNWBlZmYSDofNLOGKBarK2fF/n+DE7tX4yk7jjPZw+9RF9Bwzg7yFOezavZtn18ewN/A/AIiw+BnoXE1f2yZsRhDCFsKAYVjxpKZjWKy4YpKwOZw4ImLIGP1bHK4IPTdQRESkEdAcrCsU8PvweU9Tce4EQX8lgapK1rw0jlsmz8FXUcLc02MoCTfDIEg/+8fcGbcFq68QwkEcUR6sjgiC1T6qfWWcOZRHYocbSRv8wL+CVDkutweLxaJgJSIi0gjo1oZvcOFZgV/ncsfT4/s5uGISsVjtBAJBys4cZ/UfRlHw6T8ZaF9JR8tnPOj63wyL/AcRlGOEQ1gsNmyuaAb8z7m07JqJI8KNv7KU0we3kL/kSXzesyadpYiIiFwrGsG6SKCqnB1vP4G/vJg+Wbm17txzRnuIa9WV3cXxrKi6k+/Z/h89QnkAZFi30tO+ncjY5mB4qPaVERWfAoZBdEJbvtj4BunDfwlA0eF8DIsFR1QzbA4nVAZMOVcRERG5NhSwLlJVdpbCzzfgryyteYiyyx2Pz1vEu3Of4PVPOrGv6k4ANlcPpLs1D8MAw2Kheaf+3HjvH/h0xf+h7OxxItwJdBn8M77Y+Ab2iBiiPMn0ycol6K8Azgc2iz0CKDfxjEVERKSuKWBdxOqIpFlqOoX7NlK4bxN5C3Nodds0cl99l7XH0wljwUqQmyM+ZgDLMYzz2xkWK2VnjwHQ+wdPkf/2TOwRMXhSbyBmzIyaievn51h9NSqm51SJiIg0PgpYF7mwwGfewhwK921kZX4R76zdQGXo/LIL3SIOcFfEKty+g2AAhgXDsBIOBqgoPsmal8bV3F1YO1SJiIhIU6FJ7t/gQshKaNcHd/gslSEHLW0F/DRhEeOjF/0rXBnYnFHcMnkOUfEpWGx2wsFqyouOs+alcVSVnVWwEhERaaI0gnVZBu0cXzI57k3aOY7gcEbgL/djWG1Y7U4GTV9CUsd+xLfJYM1L46g8V0goWI3VFoEzKs7s4kVERMQkCljfwOctIm9hDmcO5WGxO+jd0knZGQehoB+nO4Gk5C50G/oLkjr2AyC2ZSdun7qIne8+i8VqI2PUr3HFJJp8FiIiImIWBayLXAhXhfs2AWGadx5A+rBfsPsfL1C4byPBQBVWm5OY5u1rbRfbshP9fvR7rcIuIiIimoN1sUBVBcXHdnMhXPXJyiU2uQt9snJp3nkAhmFQWrCfQFXFJdvq4cwiIiICGsG6hMvtoXmXW/FX1F5o9Ot3Fzoim+Fye0yuVERERK5XClgXsTmj6DX2dwT8vlqruMNXIUuXAUVERORyFLC+weXWrro4dImIiIhcTHOwREREROqYApaIiIhIHVPAEhEREaljClgiIiIidUwBS0RERKSOKWCJiIiI1LEGvUxDOBwGoLS01ORKvr1QKITX68XlcmGxKO/WF/XdHOq7OdT3+qeem6M++n4hc1zIIP9Ogw5YXq8XgNTUVJMrERERkabE6/USGxv7b783wv8pgl3HQqEQJ06cwO12YxiG2eV8K6WlpaSmpnLs2DFiYmLMLqfJUN/Nob6bQ32vf+q5Oeqj7+FwGK/XS3Jy8mVHyRr0CJbFYiElJcXsMupETEyM/ic0gfpuDvXdHOp7/VPPzXGt+365kasLdGFYREREpI4pYImIiIjUMQUskzmdTmbMmIHT6TS7lCZFfTeH+m4O9b3+qefmuJ763qAnuYuIiIhcjzSCJSIiIlLHFLBERERE6pgCloiIiEgdU8ASERERqWMKWCZZv349I0eOJDk5GcMwWLp0qdklNXq5ubn069cPt9tNUlISo0ePZu/evWaX1ei98sor9OjRo2bhv/79+7NixQqzy2pynn32WQzDYPr06WaX0qg9/vjjGIZR65WWlmZ2WU3C8ePHmThxIvHx8URERNC9e3e2bdtmWj0KWCYpLy8nIyOD2bNnm11Kk7Fu3TqmTJnC5s2bWbVqFdXV1dx1112Ul5ebXVqjlpKSwrPPPkteXh7btm1j0KBBjBo1ik8//dTs0pqMrVu38pe//IUePXqYXUqT0K1bN06ePFnz+vDDD80uqdErLi5mwIAB2O12VqxYwWeffcYLL7xAs2bNTKupQT8qpyEbOnQoQ4cONbuMJuX999+v9X7+/PkkJSWRl5fHbbfdZlJVjd/IkSNrvX/66ad55ZVX2Lx5M926dTOpqqajrKyMCRMm8Oqrr/LUU0+ZXU6TYLPZaNGihdllNCnPPfccqampzJs3r+azdu3amViRRrCkCSspKQHA4/GYXEnTEQwGeeuttygvL6d///5ml9MkTJkyheHDh3PHHXeYXUqTsX//fpKTk2nfvj0TJkzg6NGjZpfU6L377rv07duXH/zgByQlJdGrVy9effVVU2vSCJY0SaFQiOnTpzNgwADS09PNLqfR27VrF/3798fn8xEdHc2SJUu44YYbzC6r0XvrrbfYvn07W7duNbuUJuOmm25i/vz5dOnShZMnTzJz5kxuvfVWdu/ejdvtNru8RuuLL77glVde4ZFHHuHXv/41W7duZdq0aTgcDiZNmmRKTQpY0iRNmTKF3bt3a25EPenSpQv5+fmUlJTw97//nUmTJrFu3TqFrGvo2LFjPPTQQ6xatQqXy2V2OU3G16d+9OjRg5tuuok2bdqwaNEisrOzTayscQuFQvTt25dnnnkGgF69erF7927+/Oc/mxawdIlQmpyf//znLFu2jDVr1pCSkmJ2OU2Cw+GgY8eO9OnTh9zcXDIyMpg1a5bZZTVqeXl5nDp1it69e2Oz2bDZbKxbt44//vGP2Gw2gsGg2SU2CXFxcXTu3JkDBw6YXUqj1rJly0v+wda1a1dTL89qBEuajHA4zNSpU1myZAlr1641fQJkUxYKhaiqqjK7jEZt8ODB7Nq1q9ZnP/nJT0hLS+NXv/oVVqvVpMqalrKyMg4ePMi9995rdimN2oABAy5Zdmffvn20adPGpIoUsExTVlZW6180hw4dIj8/H4/HQ+vWrU2srPGaMmUKCxYs4J133sHtdlNQUABAbGwsERERJlfXeOXk5DB06FBat26N1+tlwYIFrF27lpUrV5pdWqPmdrsvmV8YFRVFfHy85h1eQ48++igjR46kTZs2nDhxghkzZmC1Whk/frzZpTVqDz/8MLfccgvPPPMM48aNY8uWLcyZM4c5c+aYV1RYTLFmzZowcMlr0qRJZpfWaH1Tv4HwvHnzzC6tUZs8eXK4TZs2YYfDEU5MTAwPHjw4/MEHH5hdVpM0cODA8EMPPWR2GY1aVlZWuGXLlmGHwxFu1apVOCsrK3zgwAGzy2oS3nvvvXB6enrY6XSG09LSwnPmzDG1HiMcDodNynYiIiIijZImuYuIiIjUMQUsERERkTqmgCUiIiJSxxSwREREROqYApaIiIhIHVPAEhEREaljClgiIiIidUwBS0RERKSOKWCJSKNnGAZLly69psfIzMxk+vTp1/QYItJwKGCJSJ356KOPsFqtDB8+/Kq3bdu2LS+++GLdF/UfjBw5krvvvvsbv9uwYQOGYbBz5856rkpEGjoFLBGpM3PnzmXq1KmsX7+eEydOmF3OFcnOzmbVqlV8+eWXl3w3b948+vbtS48ePUyoTEQaMgUsEakTZWVlLFy4kJ/97GcMHz6c+fPnX/Kb9957j379+uFyuUhISOCee+4Bzl9eO3LkCA8//DCGYWAYBgCPP/44PXv2rLWPF198kbZt29a837p1K3feeScJCQnExsYycOBAtm/ffsV1jxgxgsTExEvqLSsrY/HixWRnZ1NUVMT48eNp1aoVkZGRdO/enTfffPOy+/2my5JxcXG1jnPs2DHGjRtHXFwcHo+HUaNGcfjw4Zrv165dy4033khUVBRxcXEMGDCAI0eOXPG5iYh5FLBEpE4sWrSItLQ0unTpwsSJE3nttdf4+rPkly9fzj333MOwYcPYsWMHq1ev5sYbbwTg7bffJiUlhSeeeIKTJ09y8uTJKz6u1+tl0qRJfPjhh2zevJlOnToxbNgwvF7vFW1vs9n48Y9/zPz582vVu3jxYoLBIOPHj8fn89GnTx+WL1/O7t27uf/++7n33nvZsmXLFdd5serqaoYMGYLb7WbDhg1s3LiR6Oho7r77bvx+P4FAgNGjRzNw4EB27tzJRx99xP33318TPkXk+mYzuwARaRzmzp3LxIkTAbj77rspKSlh3bp1ZGZmAvD000/zwx/+kJkzZ9Zsk5GRAYDH48FqteJ2u2nRosVVHXfQoEG13s+ZM4e4uDjWrVvHiBEjrmgfkydP5vnnn69V77x58xg7diyxsbHExsby6KOP1vx+6tSprFy5kkWLFtWExKu1cOFCQqEQf/3rX2tC07x584iLi2Pt2rX07duXkpISRowYQYcOHQDo2rXrtzqWiNQ/jWCJyHe2d+9etmzZwvjx44Hzo0JZWVnMnTu35jf5+fkMHjy4zo9dWFjIT3/6Uzp16kRsbCwxMTGUlZVx9OjRK95HWloat9xyC6+99hoABw4cYMOGDWRnZwMQDAZ58skn6d69Ox6Ph+joaFauXHlVx7jYJ598woEDB3C73URHRxMdHY3H48Hn83Hw4EE8Hg/33XcfQ4YMYeTIkcyaNeuqRvZExFwawRKR72zu3LkEAgGSk5NrPguHwzidTl5++WViY2OJiIi46v1aLJZal+3g/KW1r5s0aRJFRUXMmjWLNm3a4HQ66d+/P36//6qOlZ2dzdSpU5k9ezbz5s2jQ4cODBw4EIDnn3+eWbNm8eKLL9K9e3eioqKYPn36ZY9hGMZlay8rK6NPnz787W9/u2TbxMRE4PyI1rRp03j//fdZuHAhjz32GKtWreLmm2++qnMTkfqnESwR+U4CgQCvv/46L7zwAvn5+TWvTz75hOTk5JrJ4D169GD16tX/dj8Oh4NgMFjrs8TERAoKCmoFlfz8/Fq/2bhxI9OmTWPYsGF069YNp9PJmTNnrvo8xo0bh8ViYcGCBbz++utMnjy55tLdxo0bGTVqFBMnTiQjI4P27duzb9++y+4vMTGx1ojT/v37qaioqHnfu3dv9u/fT1JSEh07dqz1io2Nrfldr169yMnJYdOmTaSnp7NgwYKrPjcRqX8KWCLynSxbtozi4mKys7NJT0+v9Ro7dmzNZcIZM2bw5ptvMmPGDPbs2cOuXbt47rnnavbTtm1b1q9fz/Hjx2sCUmZmJqdPn+b3v/89Bw8eZPbs2axYsaLW8Tt16sQbb7zBnj17+Pjjj5kwYcK3Gi2Ljo4mKyuLnJwcTp48yX333VfrGKtWrWLTpk3s2bOHBx54gMLCwsvub9CgQbz88svs2LGDbdu28eCDD2K322u+nzBhAgkJCYwaNYoNGzZw6NAh1q5dy7Rp0/jyyy85dOgQOTk5fPTRRxw5coQPPviA/fv3ax6WSAOhgCUi38ncuXO54447ao26XDB27Fi2bdvGzp07yczMZPHixbz77rv07NmTQYMG1boL74knnuDw4cN06NCh5hJZ165d+dOf/sTs2bPJyMhgy5YttSabXzh+cXExvXv35t5772XatGkkJSV9q3PJzs6muLiYIUOG1Lrc+dhjj9G7d2+GDBlCZmYmLVq0YPTo0Zfd1wsvvEBqaiq33norP/rRj3j00UeJjIys+T4yMpL169fTunVrxowZQ9euXcnOzsbn8xETE0NkZCSff/45Y8eOpXPnztx///1MmTKFBx544Fudm4jULyN88SQBEREREflONIIlIiIiUscUsERERETqmAKWiIiISB1TwBIRERGpYwpYIiIiInVMAUtERESkjilgiYiIiNQxBSwRERGROqaAJSIiIlLHFLBERERE6pgCloiIiEgd+//k9imrMpZw4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "plt.scatter(y_test, y_pred, alpha=0.7, color='#a3630f', marker='x', label='Predicted vs Actual')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='#25599c', linestyle='--', label='Perfect Fit')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Model Predictions vs Ground Truth')\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88cc341a-8869-4dd9-be77-ef9bf2d8b5c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
